Mode shapes (MSs) have been extensively used to detect structural damage. This paper presents a new non-model-based damage identification method that uses measured MSs to identify damage in plates. A MS damage index (MSDI) is proposed to identify damage near regions with consistently high values of MSDIs associated with MSs of different modes. A MS of a pseudo-undamaged plate can be constructed for damage identification using a polynomial of a properly determined order that fits the corresponding MS of a damaged plate, if the associated undamaged plate is geometrically smooth and made of materials that have no stiffness and mass discontinuities. It is shown that comparing a MS of a damaged plate with that of a pseudo-undamaged plate is better for damage identification than with that of an undamaged plate. Effectiveness and robustness of the proposed method for identifying damage of different positions and areas are numerically investigated using different MSs; effects of crucial factors that determine effectiveness of the proposed method are also numerically investigated. Damage in the form of a machined thickness reduction area was introduced to an aluminum plate; it was successfully identified by the proposed method using measured MSs of the damaged plate.
Maintenance and repairing in actual engineering for long-term used structures, such as pipelines and bridges, make structural damage detection indispensable, as an unanticipated damage may give rise to a disaster, leading to huge economic loss. A new approach for detecting structural damage using transmissibility together with hierarchical clustering and similarity analysis is proposed in this study. Transmissibility is derived from the structural dynamic responses characterizing the structural state. First, for damage detection analysis, hierarchical clustering analysis is adopted to discriminate the damaged scenarios from an unsupervised perspective, taking transmissibility as feature for discriminating damaged patterns from undamaged ones. This is unlike directly predicting the structural damage from the indicators manifestation, as sometimes this can be vague due to the small difference between damaged scenarios and the intact baseline. For comparison reasons, cosine similarity measure and distance measure are also adopted to draw out sensitive indicators, and correspondingly, these indicators will manifest in recognizing damaged patterns from the intact baseline. Finally, for verification purposes, simulated results on a 10-floor structure and experimental tests on a free-free beam are undertaken to check the suitability of the raised approach. The results of both studies are indicative of a good performance in detecting damage that might suggest potential application in actual engineering real life.
The aim of this study is to investigate the capability of the zero-lag cross-correlation imaging condition of an A0 Lamb wave mode in imaging a barely visible impact damage in a carbon fiber–reinforced polymer composite using a fully non-contact-guided wave-based non-destructive inspection system. A 16-ply (45/0/-45/90)2s carbon fiber–reinforced polymer laminate was impacted at three different locations with different impact energies using a drop ball at three drop heights causing three barely visible impact damages with different sizes. The A0 Lamb wave mode is generated inside the laminate using a circular air-coupled transducer and detected along the damaged region using a laser Doppler vibrometer. The measured wavefield is then decomposed into a forward and backward propagating wavefields by applying a frequency–wavenumber filtering post-processing technique. The decomposed wavefields are then cross-correlated in the frequency domain using zero-lag cross-correlation imaging condition producing a detailed cumulative damage image. The images obtained in frequency domain highlight the three damaged areas with higher zero-lag cross-correlation values compared to other parts of the inspected areas. The experimental investigation has shown a good correlation between the zero-lag cross-correlation imaging condition and C-scan images, which demonstrate a strong capability of guided wave zero-lag cross-correlation imaging condition technique in approximating the location and size of relatively small barely visible impact damages in thin composite structures.
In order to reduce operation and maintenance costs of aircraft, in situ structural health monitoring techniques are implemented on critical parts and assemblies. Many of these techniques rely on models considering, with various levels of complexity, the generation, propagation and interaction of ultrasonic guided waves with potential damages, in order to detect, localize and estimate damage severity. Although their potential has been extensively demonstrated on isotropic substrates, their implementation still poses a challenge for composite assemblies for which only quasi-isotropic and cross-ply composites have been considered. This is mainly due to the limitations of the models to properly predict the complex behaviour of guided waves on composites, where the assumptions behind the models actually used for damage imaging do not fully consider the impact of the anisotropy on guided wave generation and propagation. This article presents a comparative analysis of the performances of three model-based damage imaging techniques for composites previously validated on isotropic substrates. The main objective of the study is to address the interest in using more complex analytical formulations to improve the performance of imaging techniques. This is obtained by comparing three imaging techniques, each presenting different levels of complexity in their numerical formulations. Performance of (a) delay-and-sum, (b) dispersion compensation and (c) correlation-based techniques are addressed numerically and experimentally. The analysis is conducted on a unidirectional transversely isotropic laminate instrumented with four circular piezoceramic transducers. A robustness analysis of the models is performed numerically, where the effect of varying stiffness parameters and velocity is addressed. The correlation-based technique is adapted for the first time to composite laminates where the generation is considered using the pin-force model and the propagation is modelled via the use of the global matrix model. Experimental validation is carried out and the results obtained show the benefit of considering the steering effect for well-resolved multi modal damage imaging.
Diagnosis and prognosis are key issues in the application of condition based maintenance. Thus, there is a need to evaluate the condition of a machine. Physics-based models are of great interest as they give the response of a modelled system in different operating conditions. This strategy allows for the generation of synthetic data that can be used in combination with real data acquired by sensors to improve maintenance. The article presents an electromechanical model for a rotating machine, with special emphasis on the modelling of rolling element bearings. The proposed model is validated by comparing the simulation results and the experimental results in different operating conditions and different damaged states. This comparison shows good agreement, obtaining differences of up to 10% for the modelling of the whole rotating machine and less than 0.6% for the model of the bearing.
This article proposes a non-contacting measurement technique based on acoustic monitoring to detect cracks or damage within a structure by observing sound radiation using a single microphone or a beamforming array. The technique works by mounting an audio speaker inside a hollow structure, such as a wind turbine blade, and observing the sound radiated from the blade to identify damage. The primary hypothesis for this structural damage detection technique is that the structural damage (cracks, edge splits, holes, etc.) on the surface results in changes in the sound radiation characteristics of the structure. Preliminary measurements to validate the methodology were carried out on a section of a wind turbine blade containing different sized holes and cracks. An acoustic microphone array with 62 microphones was used to measure the sound radiated from the structure when an audio speaker generating random noise was placed inside a cavity emulating a wind turbine blade. A phased array beamforming technique and CLEAN-based subtraction of point spread function from a reference were employed to locate the different damage types on the test structures. The same experiment was repeated using a commercially available 48-channel acoustic ring array to compare the test results. It was shown that both the acoustic beamforming and the CLEAN-based subtraction of point spread function from reference techniques can identify the damage in the test structures with sufficiently high fidelity.
The major sources of error in the measurements of concrete structures are the gauge sensitivities, calibration accuracies, amplitude linearities, and temperature corrections to the gauge sensitivities, which are given in terms of plus–minus ranges, and the round off errors in the measured responses, which are better represented by interval bounds. An algorithm is proposed adapting the existing modified Metropolis Hastings algorithm for estimating the posterior probability of the damage indices as well as the posterior probability of the bounds of the interval parameters, when the measurements are given in terms of interval bounds. A damage index is defined for each element of the finite element model considering the parameters of each element are intervals. Methods are developed for evaluating response bounds in the finite element software ABAQUS, when the parameters of the finite element model are intervals. The proposed method is validated against reinforced concrete beams with three damage scenarios mainly (1) loss of stiffness, (2) loss of mass, and (3) loss of bond between concrete and reinforcement steel, which have been tested in our laboratory. Comparison of the prediction from the proposed method with those obtained from Bayesian analysis and interval optimization technique show improved accuracy and computational efficiency in addition to better representation of measurement uncertainties through interval bounds.
Ultrasonic guided waves are particularly interesting for SHM applications because they have the ability to propagate long distances with minimal attenuation. Using the baseline subtraction approach, the signal from a defect free structure is subtracted from the actual monitoring signal to detect and characterize defects. Low frequency guided wave SHM and the interaction of the fundamental guided wave modes with various types of defect are well documented in the literature. There are, however, only a very limited number of studies on high order modes. High frequency guided waves may enable the detection of smaller cracks relative to conventional low frequency guided wave SHM. The main difficulty at high frequency is the existence of several modes with different velocities. This study investigates the scattering of high frequency Lamb waves around a through-thickness hole with a view to developing a highly sensitive SHM system for safety-critical components. A 3D finite element model of a 305 x 305 x 1.6 mm aluminum plate was used to determine the scattered field generated by cracks on the circumference of a through-thickness hole in the middle of the plate. Crack properties such as orientation, length and depth were studied. A subset of the finite element simulations were validated against experimental results. The experimental setup comprised a classic contact piezoelectric transducer bonded on the side of the plate and a laser interferometer detector.
This article presents a two-dimensional, non-contact, areal scanning system to image and quantify multiple sites of damage in isotropic plates using reverse-time migration with a normalized zero-lag cross-correlation imaging condition. The hybrid system composed of a single piezoelectric actuator mounted onto the structure and a laser Doppler vibrometer for two-dimensional scan. The laser Doppler vibrometer scanned a region in the vicinity of the lead zirconate titanate actuator to capture the scattered wavefield introduced by the damage. The proposed damage imaging technique takes into account the amplitude, phase, and all the frequency content of the single-mode Lamb waves propagating in the plate; thus, the size of multiple sites of damage can be imaged without bias, regardless of the damage locations. Damage image quality was used as a metric to compare two-dimensional areal scans and linear scans as well as to compare the proposed method with existing imaging conditions. The experimental results show that the two-dimensional reverse-time migration/normalized zero-lag cross-correlation technique is capable of imaging and quantification of multiple damage sites in an aluminum plate using a single lead zirconate titanate actuator and a nearby, areal laser Doppler vibrometer scan.
Detection and characterization of delamination damage are of great importance to the assurance of structural safety. This work investigates the potential of a baseline-free structural health monitoring technique based on higher harmonics resulting from the nonlinear interaction of guided wave and a delamination. The nonlinearity considered in this study arises from the clapping of the sub-laminates in the delaminated region, which generates contact acoustic nonlinearity. Both explicit finite element simulations and experimental tests are conducted on composite laminates containing a delamination of different sizes and at different through-thickness locations. The results show that the interaction between the fundamental asymmetric mode (A0) of guided wave and a delamination generates contact acoustic nonlinearity in the form of higher harmonics, which provides a good measure for identifying the existence of delaminations and determining their sizes in laminated composite beams. This new insight into the generation mechanisms of nonlinear higher order harmonics in composite laminates will enhance the detection and monitoring of damage in composite structures.
In order to overcome the difficulties in applying traditional time-of-arrival techniques for locating acoustic emission events in complex structures and materials, a technique termed ‘Delta-t mapping’ was developed. This article presents a significant improvement on this, in which the difficulties in identifying the precise arrival time of an acoustic emission signal are addressed by incorporating the Akaike information criteria. The performance of the time of arrival, the Delta-t mapping and the Akaike information criteria Delta-t mapping techniques is assessed by locating artificial acoustic emission sources, fatigue damage and impact events in aluminium and composite materials, respectively. For all investigations conducted, the improved Akaike information criteria Delta-t technique shows a reduction in average Euclidean source location error irrespective of material or source type. For locating Hsu–Nielsen sources on a complex aluminium specimen, the average source location error (Euclidean) is 32.6 (time of arrival), 5.8 (Delta-t) and 3 mm (Akaike information criteria Delta-t). For locating fatigue damage on the same specimen, the average error is 20.2 (time of arrival), 4.2 (Delta-t) and 3.4 mm (Akaike information criteria Delta-t). For locating Hsu–Nielsen sources on a composite panel, the average error is 19.3 (time of arrival), 18.9 (Delta-t) and 4.2 mm (Akaike information criteria Delta-t). Finally, the Akaike information criteria Delta-t mapping technique had the lowest average error (3.3 mm) when locating impact events when compared with the Delta-t (18.9 mm) and time of arrival (124.7 mm) techniques. Overall, the Akaike information criteria Delta-t mapping technique is the only technique which demonstrates consistently the lowest average source location error (greatest average error of 4.2 mm) when compared with the Delta-t (greatest average error of 18.9 mm) and time of arrival (greatest average error of 124.7 mm) techniques. These results demonstrate that the Akaike information criteria Delta-t mapping technique is a viable option for acoustic emission source location, increasing the accuracy and likelihood of damage detection, irrespective of material, geometry and source type.
The impedance-based structural health monitoring method has become a promising and attractive tool for damage identification and is considered a nondestructive evaluation technique. However, conventional impedance-based structural health monitoring studies have mainly focused on structural damage identification but not so much on statistical modeling approaches in order to determine a threshold for the decision making of the damage detection system. In this study, the impedance-based structural health monitoring technique is used in a damage detection problem considering temperature variation effects. For this aim, three aluminum 2024-T3 plates were instrumented with small lead zirconate titanate patches close to their borders, and damage was introduced in the central position of the plates, with temperature ranging from –10°C to 60°C. This article proposes a method to statistically determine a threshold for damage detection purposes using concepts of statistical process control, as well as confidence intervals and normality tests in order to obtain a diagnosis with a previously determined confidence level. Thus, this work presents a sensitivity evaluation of the impedance-based structural health monitoring technique as applied to aluminum plates under varying temperature. With the technique proposed, damage threshold levels are determined so that lead zirconate titanate patches placed approximately 280 mm from the damage inserted were able to detect saw cuts of approximately 7 mm long, with 95% confidence intervals inside the temperature range considered.
In view of an extensive literature about guided waves–based structural health monitoring of plate-like structures made of metallic and composite materials, a lack of information is pointed out regarding an effective and universally accepted approach for characterizing capability and reliability in detecting, localizing and sizing in-service damages. On the other hand, in the frame of traditional non-destructive testing systems, capability is typically expressed by means of suitable ‘probability of detection’ curves based on Berens’ model, where a linear relationship is established between probability of detection and flaw size. Although the uncertain factors are usually different between a non-destructive inspection technique and a structural health monitoring approach, it seems that a similar mathematical framework could be assumed. From this point of view, this research investigates the feasibility of application of the very recent ‘multi-parameter’ probability of detection approach, developed within the traditional non-destructive testing field, to guided waves–based structural health monitoring. In particular, numerical simulations as well as experimental responses from flawed aluminium alloy plates were combined to bring about a ‘master’ probability of detection curve. Once established, this curve can be used to study the intrinsic capability of the system in terms of probability of detection curves, overcoming the intrinsic limitation of a single predictor (like the crack size) and a statistical model typically based upon a linear behaviour between the predictor and the response.
In this paper, we propose an electrical impedance tomography (EIT)-based multifunctional surface sensing system, or sensing skin, for structural health monitoring. More specifically, the EIT-based sensing skin is developed for detecting and localizing the ingress of chlorides and cracking: two phenomena which are of concern in many structures, including reinforced concrete structures. The multifunctional sensing skin is made of two layers: one layer is sensitive to both chlorides and cracking, and the other layer is sensitive to cracking only. In the experiments, the sensing skin is tested on a polymeric and concrete substrate. The results demonstrate the feasibility of using the multifunctional multi-layer sensing skin for detecting and localizing corrosive elements and cracking, and for distinguishing between them.
Piezoceramic-based smart aggregate has been widely used to evaluate early-age concrete strength and to detect damage in concrete structures. In these structural health monitoring systems, they are generally verified and calibrated through experiments under load-free condition. However, the stress levels of actual concrete members are different. The microstructures of concrete will change with the variation of external load, and the high-frequency waves used in the monitoring system may be highly sensitive to these changes. In this study, the effects of axial compressive loading on the monitoring results are investigated. Specifically, three loading cases, that is, single cycle load, cyclic load, and step-by-step load, are employed to stress the concrete specimens embedded with smart aggregates. The amplitude and velocity of monitoring signals were measured before, during, and after each loading case. The test results show that the axial load lower than 30% of failure load still have a significant impact on the received signals. The amplitude attenuation is dependent on both frequency and load history, while the velocity is highly stress-dependent. The results indicate that the baselines of monitoring signals obtained from the same concrete structure in its healthy state can vary under different stress levels. The axial load variation should be carefully considered during the monitoring process. This study also provides a potential method to assess stress state in concrete structures using smart aggregates.
This study evaluated fatigue-induced disbond areas in carbon fiber–reinforced polymer double-lap joints using embedded fiber Bragg grating sensors. When the disbond grew by cyclic loading, the embedded fiber Bragg grating sensors yielded reflection spectra having two peaks representing a step-like strain distribution generated by the disbond; the peak at the shorter wavelength corresponded to the unloaded disbond region. The ratio of the peak intensity at the shorter wavelength to that at the longer wavelength increased gradually with increasing disbond length. The relationship between the peak intensity ratio and the disbond length was analyzed by coupled structural–optical analysis and was validated by comparing analytical peak intensity ratio with the experiment results. The disbond length was then estimated from the measured spectra based on this analytical calibration relationship, but the estimated disbond area exceeded that observed using the ultrasonic C-scan technique. Additional experiments including destructive observation of the adhesive suggested that an embedded fiber Bragg grating sensor could detect a moving disbond tip earlier than conventional nondestructive techniques.
Structural health monitoring is widely applied in industrial sectors as it reduces costs associated with maintenance intervals and manual inspections of damage in sensitive structures, while enhancing their operation safety. A major concern and current challenge in developing "robust" structural health monitoring systems, however, is the impact of uncertainty in the input training parameters on the accuracy and reliability of predictions. The aim of this article is to adapt an advanced statistical pattern recognition technique capable of considering variations in input parameters and arriving at a new structural health monitoring system more immune to the effect of uncertainty. Gaussian processes have been implemented to predict the state of damage in a typical composite airfoil structure. Different covariance functions were evaluated during the training stage of structural health monitoring. Results through a case study showed a remarkable capability of the Gaussian process–based approach to deal with uncertainty in the pattern recognition problem in structural health monitoring of a multi-layer composite airfoil structure. To illustrate robustness advantage of the approach as compared to conventional neural network models, the damage size and location prediction accuracy of the Gaussian process structural health monitoring has been compared to multi-layer perceptron neural networks. Some practical insights and limitations of the approach have also been outlined.
This study exploits the feasibility of imaging zones of local porosity/voids simulated by introducing microspheres during layup of a unidirectional carbon fiber–reinforced polymer composite panel. A fully non-contact hybrid system primarily composed of an air-coupled transducer and a laser Doppler vibrometer was used for imaging the local porosity/void zones from the guided wave response. To improve image resolution, several preprocessing techniques are performed. The wavefield reconstructed from the laser Doppler vibrometer measurements was first "denoised" using a one-dimensional wavelet transform in the time domain followed by a two-dimensional wavelet transform in the spatial domain. From the total wavefield, the much weaker backscattered waves were separated from the stronger incident wave by frequency–wavenumber domain filtering. In order to further enhance the signal-to-noise ratio and sharpen the image, the attenuation of incident wave propagation to the damage site was compensated through two proposed weight functions. Finally, a zero-lag cross-correlation was performed for imaging the zone where the compensated incident and backscattered waves were in phase. This improved imaging condition, the "denoised" weighted zero-lag cross-correlation, was proposed and tested for defect imaging in the composite panel with eight intentionally introduced zones of high porosity/voids of varying diameters (1.59–6.35 mm) and depths (0.36–1.08 mm). As expected, the sensitivity of the non-contact air-coupled transducer/laser Doppler vibrometer hybrid system was limited by the wavelength of the excitation signal. The system incorporated with the denoised weighted zero-lag cross-correlation imaging condition for guided wave interrogation gave similar image quality in comparison with that by the immersion C-scan.
A baseline-free damage identification method is proposed to identify damages in metallic sandwich panels with truss core in the article. The method is based on flexibility matrix and gapped smoothing method, with damage index defined DIm. The weight coefficient m is introduced to consider the effect of damages on both low-order modes and high-order modes. Numerical simulations and experiments are conducted to evaluate the present method. Besides, damage index
This article reports on the characterization of delamination damages in composite laminates using wave visualization method. A combination of plate-guided ultrasound and air-coupled ultrasonics is used to locate and visualize delaminations. The study focuses on the physics of Lamb wave propagation and interaction with delaminations at various through-thickness locations and positions. Three-dimensional finite element simulations are used to study, in detail, the changes in wave features such as mode velocity, wavelength and wave refraction in the delamination region. These wave features provide information on the location, position and orientation of the delamination. These studies are validated by experimental measurements. The influence of position of source and delamination on wave refraction in the delamination region is examined. This method also correlates the results obtained from experiments and finite element simulations to theoretical dispersion curves in order to distinctly determine the delamination location.
Structural health monitoring offers an attractive tool for condition assessment, fault prognosis and life-cycle management of wind turbine components. However, owing to the intense loading conditions, geometrical nonlinearities, complex material properties and the lack of real-time information on induced structural response, damage detection and characterization of structural components comprise a challenging task. This study is focused on the problem of damage detection for a small-scale wind turbine (Sonkyo Energy Windspot 3.5 kW) experimental blade. To this end, the blade is dynamically tested in both its nominal (healthy) condition and for artificially induced damage of varying types and intensities. The response is monitored via a set of accelerometers; the acquired signals serve for damage detection via the use of appropriate statistical and modal damage detection methods. The former rely on extraction of a characteristic statistical quantity and establishment of an associated statistical hypothesis test, while the latter rely on tracking of damage-sensitive variations of modal properties. The results indicate that statistical-based methods outperform modal-based ones, succeeding in the detection of induced damage, even at low levels.
The growing use of carbon fibre–reinforced polymer materials in aerospace and industrial applications requires the deployment of appropriate tools for detecting the occurrence and development of damage as well as for monitoring the durability of conductive composite structures. Electrical resistance tomography is a promising method in this field, since many types of fibre composite defects cause changes in the spatial distribution of conductivity. In comparison with other methods in the field of structural health monitoring, electrical resistance tomography has, at first glance, modest requirements for sensors, and thus appears to be a useful instrument in this field. Most of the previously conducted experiments in this field relied on the stand-alone hardware thereby did not contribute to a technological readiness level of the electrical resistance tomography. The article describes an experiment conducted on a carbon fibre–reinforced polymer composite specimen in laboratory conditions nevertheless with minimum usage of stand-alone equipment. Attention is focused on the first steps in the development of hardware, namely on the instrumental amplifier with active shielding and the voltage controlled current source. Experimental verification of usability of these components along with observed common mode voltage error and dynamic range is useful for next development of more complicated device such as a multiplexer. Greater attention is also paid to the implementation of electrodes, as these are a key part. The main contributions of proposed work lie in the usability verification of the key hardware components with the help of basic image reconstruction of the real damage.
Wind-induced operational variability is one of the major challenges for structural health monitoring of slender engineering structures like aircraft wings or wind turbine blades. Damage sensitive features often show an even bigger sensitivity to operational variability. In this study a composite cantilever was subjected to multiple mass configurations, velocities and angles of attack in a controlled wind tunnel environment. A small-scale impact damage was introduced to the specimen and the structural response measurements were repeated. The proposed damage detection methodology is based on automated operational modal analysis. A novel baseline preparation procedure is described that reduces the amount of user interaction to the provision of a single consistency threshold. The procedure starts with an indeterminate number of operational modal analysis identifications from a large number of datasets and returns a complete baseline matrix of natural frequencies and damping ratios that is suitable for subsequent anomaly detection. Mahalanobis distance-based anomaly detection is then applied to successfully detect the damage under varying severities of operational variability and with various degrees of knowledge about the present operational conditions. The damage detection capabilities of the proposed methodology were found to be excellent under varying velocities and angles of attack. Damage detection was less successful under joint mass and wind variability but could be significantly improved through the provision of the currently encountered operational conditions.
A fatigue crack and its precursor often serves as a source of nonlinear mechanism for ultrasonic waves, and nonlinear ultrasonic techniques have been widely studied to detect fatigue crack at its very early stage. In this study, a wireless sensor node based on nonlinear ultrasonics is developed specifically for fatigue crack detection: (1) through packaged piezoelectric transducers, ultrasonic waves at two distinctive frequencies are generated, and their modulation due to a microcrack (less than 0.1 mm in width) is detected; (2) an autonomous reference-free crack detection algorithm is developed and embedded into the sensor node, so that users can simply "stick" the sensor to a target structure and automatically "detect" a fatigue crack without relying on any history data of the target structure; and (3) the whole design of the sensor node is fulfilled in a low-power working strategy. The performance of the sensor node is experimentally validated using aluminum plates with real fatigue cracks and compared with that of a conventional wired system. Furthermore, a field test in Yeongjong Grand Bridge in South Korea has been conducted with the developed sensor nodes.
This work provides a system-level investigation into the use of embedded fiber Bragg grating optical sensors as a viable sensing architecture for the structural health monitoring of composite structures. The practical aspects of the embedding process are documented for both carbon fiber–reinforced polymer and glass fiber–reinforced polymer structures manufactured by both oven vacuum bag and vacuum-assisted resin transfer method processes. Initially, embedded specimens were subject to long-term water submersion to verify performance in an underwater environment. A larger, more complex jointed specimen was also fabricated with a fully embedded sensor network of fiber Bragg gratings and subjected to incrementally induced bearing damage. Using commercially available interrogation hardware, a damage detection structural health monitoring algorithm was developed and deployed. The results permit statistically precise detection of low levels of connection damage in the composite specimen.
Guided waves have been used for structural health monitoring to detect damage or defects in structures. However, guided wave signals often involve multiple modes and noise. Extracting meaningful damage information from the received guided wave signal becomes very challenging, especially when some of the modes overlap. The aim of this study is to develop an effective way to deal with noisy guided-wave signals for damage detection as well as for de-noising. To achieve this goal, a robust sparse Bayesian learning algorithm is adopted. One of the many merits of this technique is its good performance against noise. First, a Gabor dictionary is designed based on the information of the noisy signal. Each atom of this dictionary is a modulated Gaussian pulse. Then the robust sparse Bayesian learning technique is used to efficiently decompose the guided wave signal. After signal decomposition, a two-step matching scheme is proposed to extract meaningful waveforms for damage detection and localization. Results from numerical simulations and experiments on isotropic aluminum plate structures are presented to verify the effectiveness of the proposed approach in mode identification and signal de-noising for damage detection.
This article introduces a methodology for the detection and localization of structural defects in solid media using morphological demixing algorithms. The demixing algorithms are designed to separate spatiotemporal response data into two morphologically antithetical components: one contribution captures the spatially sparse and temporally persistent features of the medium’s response, while the other provides a representation of the dominant, globally smooth component as it would be observed in a defect-free medium. Within the demixing paradigm, we explore two methods: in the first, we cast the demixing task in terms of a group Lasso regularization problem with simply structured orthonormal dictionaries; the second method makes use of a more morphologically germane dictionary whose additional structure allows for the localization of defects whose signature may be highly elusive, for example, buried in noise or masked by competing features. After the demixing is complete, an automatic visualization tool highlights the regions associated with potential anomalies and outputs their local coordinates. Since the method does not invoke any knowledge of the material properties of the medium, or of its behavior in its pristine conditions, and is solely based on data processing of current wavefield information, it is endowed with significant model agnostic and baseline-free attributes. These properties are desirable in systems where there exists limited or unreliable a priori knowledge of the constitutive model, when the physical domain is highly heterogeneous or compromised by large damage zones, or when accurate baseline simulations are unavailable. The efficacy of the proposed method is evaluated against synthetically generated data and experimental data obtained using a scanning laser Doppler vibrometer.
Materials such as aluminum alloys and carbon composites are widely used in aircraft structures. In the case of the use of Al-alloys in aircraft structures, fatigue cracks occur because of excessive and repeated loading and vibrations experienced during frequent flights. Meanwhile, composite materials are also damaged—for example, impact damage and debonding—and have defects, including voids, prepreg gaps, and overlaps created during the manufacturing process. Ultrasonic propagation imaging is a damage visualization technique that is used in structural inspections employing a laser scanning system and ultrasonic sensors. However, conventional ultrasonic propagation imaging or other scanning systems, such as a scanning laser Doppler vibrometer, only permit a single area to be inspected at one time. It is also difficult to inspect inaccessible areas, such as the upper skins of the aircraft wings. In this work, we describe a multi-area scanning ultrasonic propagation imaging system built in a hangar that is able to rapidly scan at a pulse repetition rate of 20 kHz. After acquiring the generated ultrasonic wave signal induced by laser excitation, ultrasonic propagation imaging videos for the in-plate guided wave are displayed. Finally, internal damage can be identified in a damage visualization platform. The developed multi-area scanning ultrasonic propagation imaging system is demonstrated by performing simultaneous inspections on two areas containing manufacturing defects in a large carbon/epoxy laminate. We also performed a demonstration of the hangar-based multi-area scanning ultrasonic propagation imaging system on an actual aircraft containing back surface cracks. The multi-area scanning ultrasonic propagation imaging system with tilting mirror systems installed in the hangar ceiling permitted a clear visualization of the damage. The damage visualization results confirm that the proposed multi-area scanning ultrasonic propagation imaging system and approach have excellent applicability as a built-in ultrasonic propagation imaging system for a Smart Hangar, which is a future structural health monitoring solution that will be used to realize a full-scale structural inspection of an actual aircraft.
Nonlinearities in the dynamical behavior of mechanical systems can degrade the performance of damage detection features based on a linearity assumption. In this article, a discrete Volterra model is used to monitor the prediction error of a reference model representing the healthy structure. This kind of model can separate the linear and nonlinear components of the response of a system. This property of the model is used to compare the consequences of assuming a nonlinear model during the nonlinear regime of a magneto-elastic system. Hypothesis tests are then employed to detect variations in the statistical properties of the damage features. After these analyses, conclusions are made about the application of Volterra series in damage detection.
The central message of this article is that for robust and efficient damage detection the damage sensitive features should be selected optimally in a systematic way such that only these features that contribute the most to damage detectability be retained. Furthermore, suitable transformations of the original features may also enhance damage detectability. We explore these principles using data from a wind turbine blade. Several damage extent scenarios are introduced non-destructively. Partial autocorrelation coefficients are proposed as vibration-based damage sensitive features. Scores calculated with principal component analysis of partial autocorrelation coefficients are the transformed damage sensitive features. Statistical distances between the damage sensitive feature subsets estimated from the healthy and a reference damage state are calculated with respect to a statistical threshold as a measure of optimality. The fast forward method and a genetic algorithm are used to optimize the detectability of damage by damage sensitive feature selection. The comparison between the two methods points out that fast forward offers a comparable performance at a lower computational cost. The classifiers based on the optimal features are tested on data from several previously unseen healthy and damaged cases and across a range of statistical detection thresholds. It is demonstrated that the selected principal component analysis scores of the partial autocorrelation coefficients are superior compared to the initial features and allow identifying small damage confidently.
In order to make up defects liable for the conventional monitoring of rockfill dam seepage in spatial inconsequence and low efficiency, a new monitoring system is proposed based on the heating technique incorporated in the temperature tracer method, that is, the integrated system of fiber Bragg grating temperature sensing and hydrothermal cycling. The system has a boiler as its heating device, and heated water from boiler is admitted through redistributor and circular warm pipelines, in which fiber Bragg grating sensors are embedded in advance for measuring the water temperature, thereby the seepage behavior is identified from the correlative fields of temperature and seepage. A coefficient v, according to Newton’s law of cooling, is then fitted out by pipeline cooling curves and used as a new way to identify the seepage state. The temperature–time–travel curves for the cooling period have proved by calibration tests to be, in general, consistent with the mathematical model of temperature variations under Newton’s law of cooling, thereby to inverting the seepage velocity through the fitting formula of it with v. With the test model of concentrative leakage established in regard to the location, amount of leakage passages, and leakage rate, multi-condition tests have been conducted which conclude that the proposed method is capable of positioning leakage and quantifying seepage velocity; therefore, it is valid for seepage monitoring and identification.
The potential for increased productivity offered by automated fibre placement method has opened up a wider range of applications as well as new markets for composite materials. However, like many other manufacturing methods, different flaws such as voids or delamination may still occur during or after lay-up. Therefore, the use of automated fibre placement as an open-mould process where fibre/tape material is fed brings with it a need and an opportunity to establish a reliable inspection and monitoring method to ensure structural integrity not only after fabrication but also one step earlier, during the manufacturing process. Since optical fibre–based photonic sensing technologies are increasingly common for structural health monitoring of composite structures, selection of optical fibre Bragg grating sensors for manufacturing process monitoring has been successfully implemented here. Experiments are carried out on glass fibre/high-density polyethylene laminates with embedded fibre Bragg grating in the automated fibre placement method. The lay-up process conditions are monitored by the fibre Bragg grating sensors via measuring the reflected wavelengths which are related to pressure and temperature. The results presented in this article indicate that fibre Bragg grating sensing technique can be reliably employed for online monitoring of lay-up process to ensure the quality of final product.
Rolling bearings, as important machinery components, strongly affect the operation of machines. Early bearing fault diagnosis methods commonly take time–frequency analysis as the fundamental basis, therein searching for characteristic fault frequencies based on bearing kinematics to identify fault locations. However, due to mode mixing, the characteristic frequencies are usually masked by normal frequencies and thus are difficult to extract. After time–frequency decomposition, the impact signal frequency can be distributed among multiple separation functions according to the mode mixing caused by the impact signal; therefore, it is possible to search for the shared frequency peak value in these separation functions to diagnose bearing faults. Using the wavelet transform, time–frequency analysis and blind source separation theory, this article presents a new method of determining shared frequencies, followed by identifying the faulty parts of bearings. Compared to fast independent component analysis, the sparse component analysis was better able to extract fault characteristics. The numerical simulation and the practical application test in this article obtained satisfactory results when combining the wavelet transform, intrinsic time-scale decomposition and linear clustering sparse component analysis, thereby proving the validity of this method.
One outstanding obstacle that hinders robust application of vibration-based damage identification to civil structures is that the number of sensors installed on a large civil structure is always limited, compared with the total degrees of freedom of the structure, so that the limited measured responses may not provide enough information for detecting local damage. Furthermore, developments in sensor technology make installation of heterogeneous sensors on a structure practical and feasible while every type of sensor has its own merits and drawbacks for damage identification. But the benefits of utilizing heterogeneous sensors in vibration-based damage identification have not been fully investigated. This study proposes a damage identification method by combining the response reconstruction technique with the response sensitivity–based finite element model updating method to address these issues. The number and location of heterogeneous sensors, such as accelerometers, displacement transducers, and strain gauges, are optimally and collectively determined in an optimization strategy to obtain the best reconstruction of multi-type responses of a structure using Kalman filter. After damage occurrence, radial basis function network is employed to predict the mode shapes using the modal properties extracted from the measurement data by experimental modal analysis method, and these modal properties are further used to reconstruct responses of the damaged structure. The reconstructed responses are finally used to identify the damage in terms of sensitivity-based finite element model updating. In every updating, the sparse regularization is employed to increase the identification accuracy. A simply supported overhanging steel beam composed of 40 elements serves as a numerical study to demonstrate the procedure and feasibility of the proposed method. The validation of this method is further conducted by laboratory test. Both simulation study and laboratory test show that the multi-sensing approach via response reconstruction does improve the identification accuracy of damage location and quantization considerably.
This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction.
This article describes how piezoelectric transducers embedded in the adhesive bond-line of lap-joints can be used to effectively monitor structural integrity. Various lap-joint coupons with embedded piezoelectric transducers were manufactured with and without artificial contamination at the bond-line and tested statically and cyclically. A novel scheme based on the electromechanical impedance response of the transducer was implemented to predict the failure of the tested lap-joint samples. The results from the mechanical testing indicated that monitoring the transducer’s electromechanical impedance is an effective way of predicting the failure of the bond-line. Specifically for static tests, local damage to the bond-line was consistently detected at approximately 84% of the failure load for transducers located at the center of the bond-line, whereas for transducers embedded near the edge of the bond-line, the failure of the adhesive was detected at 60% of the failure load. Moreover, preliminary fatigue tests showed that significant changes in the electromechanical impedance signals were apparent starting at 60% of the life of the bond-line. In addition to the mechanical testing, the effectiveness of the proposed electromechanical impedance–based scheme was investigated by means of a three-dimensional finite element model corresponding to the specific coupon geometry tested and through a two-dimensional analytical solution.
Extracting damage-sensitive features plays an important role in all structural health monitoring applications, as it determines the metrics on which to base decision-making with regard to operation, maintenance, damage state, and so on. This article adopts the widely employed frequency response function, both its magnitude and phase, as the selected feature source and demonstrates how the damage types and locations are able to be classified by means of Bayesian recursive confidence updating. The features are estimated from the in situ acquired vibration data on a rotating machinery test-bed, and the probabilistic models that quantify feature uncertainty are the likelihood functions in a Bayesian framework, which informs the most plausible decisions based on the collected evidence. The damage classification effort in this article specifically calculates the posterior probability, considering the prior and likelihood of data observations; posterior probabilities are then fed back as prior probabilities in the next iteration as new test data are observed. There are three ball-bearing damage conditions applied to the rotary machine test-bed, and the correct model representing the correct damage types will be selected by the model with the maximum posterior confidence. Classification via posterior probability is shown in this article to outperform traditional likelihood evaluations, and the Bayesian recursive implementation distinguishes all three conditions in this work.
During the service life of engineering structures, structural management systems attempt to manage all the information derived from regular inspections, evaluations and maintenance activities. However, the structural management systems still rely deeply on qualitative and visual inspections, which may impact the structural evaluation and, consequently, the maintenance decisions as well as the avoidance of collapses. Meanwhile, structural health monitoring arises as an effective discipline to aid the structural management, providing more reliable and quantitative information; herein, the machine learning algorithms have been implemented to expose structural anomalies from monitoring data. In particular, the Gaussian mixture models, supported by the expectation-maximization (EM) algorithm for parameter estimation, have been proposed to model the main clusters that correspond to the normal and stable state conditions of a structure when influenced by several sources of operational and environmental variations. Unfortunately, the optimal parameters determined by the EM algorithm are heavily dependent on the choice of the initial parameters. Therefore, this paper proposes a memetic algorithm based on particle swarm optimization (PSO) to improve the stability and reliability of the EM algorithm, a global EM (GEM-PSO), in searching for the optimal number of components (or data clusters) and their parameters, which enhances the damage classification performance. The superiority of the GEM-PSO approach over the state-of-the-art ones is attested on damage detection strategies implemented through the Mahalanobis and Euclidean distances, which permit one to track the outlier formation in relation to the main clusters, using real-world data sets from the Z-24 Bridge (Switzerland) and Tamar Bridge (United Kingdom).
In an earlier study, the poly-coherent composite higher order spectra (i.e. poly-coherent composite bispectrum and trispectrum) frequency domain data fusion technique was proposed to detect different rotor-related faults. All earlier vibration-based faults detection involving the application of poly-coherent composite bispectrum and trispectrum have been solely based on the notion that the measured vibration data from all measurement locations on a rotating machine are always available and intact. In reality, industrial scenarios sometimes deviate from this notion, due to faults and/or damages associated with vibration sensors or their accessories (e.g. connecting cables). Sensitivity analysis of the method to various scenarios of measured vibration data availability (i.e. complete data from all measurement locations and missing/erroneous data from certain measurement locations) is also examined through experimental and industrial cases, so as to bring out the robustness of the method.
Support vector machine, chaos theory, and particle swarm optimization are combined to build the prediction model of dam safety. The approaches are proposed to optimize the input and parameter of prediction model. First, the phase space reconstruction of prototype monitoring data series on dam behavior is implemented. The method identifying chaotic characteristics in monitoring data series is presented. Second, support vector machine is adopted to build the prediction model of dam safety. The characteristic vector of historical monitoring data, which is taken as support vector machine input, is extracted by phase space reconstruction. The chaotic particle swarm optimization algorithm is introduced to determine support vector machine parameters. A chaotic support vector machine–based prediction model of dam safety is built. Finally, the displacement behavior of one actual dam is taken as an example. The prediction capability on the built prediction model of dam displacement is evaluated. It is indicated that the proposed chaotic support vector machine–based model can provide more accurate forecasted results and is more suitable to be used to identify efficiently the dam behavior.
Fiber optic sensors have a lot to offer in the field of structural health monitoring. The most widely investigated and implemented fiber optic sensors for structural health monitoring are fiber Bragg grating and fiber optic polarimetric sensor. Fiber Bragg grating sensors provide localized strain data, thereby providing local damage information, while fiber optic polarimetric sensors are known for their capabilities of global damage monitoring for both static and dynamic loadings. However, each sensor has to be used with its own instrumentation and processing system. In this article, it is shown that an fiber Bragg grating written on a polarizing maintaining fiber can discern information from both fiber Bragg grating and fiber optic polarimetric sensors using only one decoding system. This reduces costs and complexities. Furthermore, by proper multiplexing the polarizing maintaining-fiber Bragg grating sensor, it is possible to predict the damage location in plates. The results demonstrate that the damage site can be located in two-dimensional structures using this multiplexed sensing array.
In this study, an autonomous mobile inspection system that can detect and quantify hidden voids in a secondary barrier (triplex layer) of a liquefied natural gas cargo tank is developed using lock-in thermography. The triplex layer is the secondary barrier to prevent gas leaks from a Mark III–type membrane liquefied natural gas carrier cargo tank and consists of three sub-layers: a flexible secondary barrier, a bonding layer, and a rigid secondary barrier. The proposed mobile inspection system consists of a lock-in thermography measurement unit, a mobile inspection unit, and image processing algorithms. First, thermal images are obtained using the lock-in thermography unit as the mobile inspection system maneuvers over triplex layers. Second, the raw thermal images are processed by several image processing techniques to compensate for non-uniform heating, eliminate noise components, and disregard trivial voids in accordance with the current inspection guideline. Third, the void size is more precisely quantified using an empirical mapping function that relates the void size estimated in the previous step to that measured by an independent X-ray test. The contributions of this study include the following: (1) an autonomous mobile inspection system is developed for real-time inspection of the triplex during its installation, significantly saving the inspection cost and time; (2) a suite of image processing techniques is developed, overcoming shortcomings of the existing thermography non-destructive testing techniques; and (3) the sizes as well as the locations of the hidden voids are quantified with high accuracy, reliability, and fast inspection speed.
In this research, an intelligent procedure was designed and implemented based on vibration signals for detecting and classifying prevalent faults of an internal combustion engine timing belt. The vibration signals of the timing belt were captured during operation in six different states: healthy, tooth crack, back crack, wear, separated tooth, and oil pollution. These signals were processed at three domains, namely, time, frequency, and time–frequency domains. Time-domain signals were transformed into the frequency and time–frequency domains using fast Fourier transform and wavelet transform, respectively. Then, six statistical features were extracted from vibration signals at all three domains. The extracted features were used as inputs to an artificial neural network for the primary classification of timing belt defects. Classification accuracy of artificial neural network in detecting and classifying timing belt faults in the time, frequency, and time–frequency domains have obtained 71%, 78%, and 84%, respectively. Combining separate classification accuracies from time, frequency, and time–frequency domains has been implemented using Dempster–Shafer theory of evidence. Classification accuracy based on the fusion of time- and frequency-domain classifiers was 97%, from time and time–frequency results was 98%, and from frequency and time–frequency results was also 98%, whereas the combination of results for all domains led to a >99% accuracy. Results show that the proposed methodology can detect and classify timing belt defects with high precision and reliability before failure occurrence.
Structural health monitoring allows the automated condition assessment of civil infrastructure, leading to a cost-effective management of maintenance activities. However, there is still a debate in the literature about the effectiveness of available signal processing strategies to timely assess the health state of a structure. This paper is a contribution to this debate, by presenting the application of different vibration-based damage detection methods using up-to-date multivariate statistical analysis techniques applied to data acquired from a permanently monitored long-span arch bridge. Techniques based on dynamic regression models, linear and local principal component analysis, as well as on their combinations, including, in particular, the newly proposed method based on the combination of dynamic multiple linear regressions and local principal component analysis, and, finally, a method based on the recently proposed approach of cointegration, are considered. A first effort is made to formulate these methods within a unique mathematical framework, highlighting, in particular, the relevant parameters affecting their results and proposing objective criteria for their appropriate tuning and for choosing the length of the training period. Then, the considered damage detection methods are implemented and applied to field data, seeking for damage-sensitive features in the presence of variable environmental and operational conditions. The considered techniques are applied to time histories of identified modal frequencies of the bridge and their capability to reveal structural damage of varying severity is assessed using control charts. The case of an artificially imposed non-linear correlation between the features is also considered. The results provide, for the first time in the literature, an estimation of the minimum level of damage that can be realistically detected in the bridge using dynamic signatures and up-to-date signal processing algorithms, thus contributing to a more aware use of monitoring data and reliance over related health state assessment information.
Underwater dam crack detection and classification based on visible images is a challenging task. The underwater environment is very complex with uneven illumination and serious noise problems, which often leads to the distortion of detection. In addition, there are few methods suitable for underwater dam crack classification. To solve these problems, a novel underwater dam crack detection and classification approach is proposed. Firstly, a dodging algorithm is used to eliminate the uneven illumination in the underwater visible images. Subsequently, a crack detection approach is proposed, where the local characteristics of image blocks and the global characteristics of connected domains are both used based on the analysis of the statistical properties of dam crack images. Finally, an improved evidence theory-based crack classification algorithm is proposed after the crack detection. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively in complex underwater environments.
This paper presents a temperature compensation method for Lamb wave structural health monitoring. The proposed approach considers a representation of the piezo-sensor signal through its Hilbert transform that allows one to extract the amplitude factor and the phase shift in signals caused by temperature changes. An ordinary least square (OLS) algorithm is used to estimate these unknown parameters. After estimating these parameters at each temperature in the operating range, linear functional relationships between the temperature and the estimated parameters are derived using the least squares method. A temperature compensation model is developed based on this linear relationship that allows one to reconstruct sensor signals at any arbitrary temperature. The proposed approach is validated numerically and experimentally for an anisotropic composite plate at different temperatures ranging from
Adhesive bonding is a promising joining method for composite materials. This article focuses on the use of modal content-based damage indicators to improve the sensitivity of guided waves to disbonds in the adhesive. Wave-defect interaction is modeled using frequency-domain finite element analysis in order to identify modes sensitive to adhesive degradation in joints between carbon fiber–reinforced polymer laminates. Phased array transducers and multielement array sensors designed for structural health monitoring are employed to enable preferential mode excitation and modal content extraction, respectively. The domains in dispersion curve space predicted to have good, intermediate, and no sensitivity to disbond were experimentally found to have good, intermediate, and limited sensitivity using a feature of the received signals associated with the modal amplitude or a modal amplitude ratio. Furthermore, the modal amplitude was found to decrease monotonically with increasing disbond size, demonstrating that it has potential to quantitatively size disbonds.
Vibro-acoustic modulation–based damage identification relies on the modulation of a high-frequency carrier signal by an intenser low-frequency vibration signal due to damage-induced structural nonlinearities. A time domain analysis of the vibro-acoustic modulation phenomena was presented at multiple spatial locations in an impact damaged composite skin–stiffener structure. The instantaneous amplitude and frequency of the carrier velocity response were extracted to analyze the intermodulation effects between the two excitation signals. Increased amplitude modulations at the damaged region revealed the presence, location, and length of the skin–stiffener damage. The damage hardly modulated the frequency of the carrier response. This difference in behavior was attributed to the nonlinear skin–stiffener interaction introduced by the periodic opening and closing of the damage, according to earlier research by authors on the same structure. A parametric study showed that the amplitude and phase of the amplitude modulation are dependent on the selected carrier excitation frequency, and hence the high-frequency wave field that is introduced. This work demonstrates not only the potential but also the complexity of the vibro-acoustic modulation based damage identification approach.
The procedure of damage accumulation in composites, especially during fatigue loading, is a complex phenomenon of stochastic nature which depends on a number of parameters such as type and frequency of loading, stacking sequence, material properties, and so on. Toward condition-based health monitoring and decision making, the need for not only diagnostic but also prognostic tools rises and draws increasing attention in the last few years. To this direction, we model the damage evolution in composites as a doubly stochastic hidden Markov process that manifests itself via structural health monitoring observations, that is, acoustic emission data. The damage process is modeled via an extension of the classic hidden Markov models to account for nonhomogeneity, that is, age dependence in state transitions. The observations come from acoustic emission data recorded throughout fatigue testing of open-hole carbon–epoxy coupons. A procedure that utilizes multiple observation sequences from a training dataset and estimates in a maximum likelihood sense the optimal model parameters is presented and applied in unseen data via a cross-validation rationale. Diagnostics of the most likely health state determination, average degradation level, and prognostics of the remaining useful life are among the capabilities of the presented stochastic model.
This article presents the development and the results of 1 year of implementation of a simple vibration-based structural health monitoring system for preventive conservation and condition-based maintenance of an Italian monumental masonry bell-tower. The system is based on the data recorded by a small number of high-sensitivity accelerometers, on remote automated frequency tracking and on a multivariate statistical analysis criterion for damage detection, combining data regression, principal component analysis, and novelty analysis. The analysis of monitoring data highlights the main characteristics of the response of the tower to wind, swinging bells, and low-return period earthquakes. Despite the low levels of vibration in operational conditions, the system is seen able to track the time evolution of five natural frequencies of the structure and successfully use such information for detecting anomalous deviations from normal conditions. More in general, the presented results show a promise toward a more widespread use of low-cost vibration-based monitoring systems for cultural heritage preservation.
The objective of this study was to design a multifunctional cement composite that could not only bear loads but also possessed electromechanical properties that are sensitive to damage. A mainstream approach is to disperse large quantities of conductive additives in the cement matrix, which can be costly, involve complex procedures, difficult to scale-up, and degrade concrete’s inherent mechanical properties. Instead, this research proposes a new method to design multifunctional and self-sensing concrete, which is achieved by altering the cement–aggregate interface using conductive, nano-engineered coatings. Here, a carbon nanotube–based ink solution was sprayed onto the surfaces of aggregates and then dried to form electrically conductive, thin film-coated aggregates. Then, the film-coated aggregates were used as is for casting concrete specimens. It was demonstrated experimentally that this procedure yielded concrete specimens that were not only conductive but also had electrical properties that varied in response to applied physical damage. An electrical impedance tomography algorithm was also implemented and used for estimating their spatial resistivity distributions. Since the electrical properties at every location of the film-enhanced concrete were sensitive to damage, electrical impedance tomography was able to produce electrical resistivity maps that indicated the locations and severities of damage. Multiple concrete cylinder, plate, and beam specimens were cast and tested for validating the self-sensing properties of film-enhanced concrete and the spatial damage detection capabilities of the electrical impedance tomography algorithm.
The risk of false calls of structural health monitoring systems is as much important for their application as their damage detection capabilities. Structural health monitoring based on guided waves propagation is particularly vulnerable to false calls. Signals acquired for piezoelectric transducer networks can be changed by many factors other than damage, for example, environmental factors or those related to the transducers’ aging or degradation of the transducers’ bonding with the monitored structure. A lot of studies were devoted to examine non-damage-related influences on structural health monitoring systems based on Lamb waves and to compensate the undesired effects. Most of compensation methods act on the level of the signal, that is, for a given factor influencing performance of piezoelectric transducers, signals are transformed to match the corresponding baselines. After such compensation procedure, the Damage Indices are calculated for the purpose of damage detection. In order to compensate the impact of all of non-damage-related factors, all of them need to be, at least, recognized. In this article, a different technique to compensate changes of Damage Indices values caused by factors other than damage is proposed. The method does not involve any operation on signals, but on the Damage Indices themselves. The factors causing Damage Indices’ changes neither have to be measured nor are even known. The capabilities of the method have been evaluated on the example of fatigue cracks detection in laboratory specimen tests and using results obtained during Full-Scale Fatigue Test of an aircraft.
Utilizing inverse uncertainty quantification techniques, structural health monitoring (SHM) can be integrated with damage progression models to form a probabilistic prediction of a structure’s remaining useful life (RUL). However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In this paper, high-fidelity fatigue crack growth simulation times are reduced by three orders of magnitude using a model based on a set of surrogate models trained via three-dimensional finite element analysis. The developed crack growth modeling approach is experimentally validated using SHM-based damage diagnosis data. A probabilistic prediction of RUL is formed for a metallic, single-edge notch tension specimen with a fatigue crack growing under mixed-mode conditions.
Local, superficial damage was detected and localized on an 8 x 2-m concrete floor slab using a structural health monitoring system. A total of 30 piezoelectric transducers, placed in a grid, transmitted and received continuous ultrasonic waves that were measured using a lock-in amplifier. Tomography was used to create images from the measured amplitude and phase of the continuous waves between all possible transducer pairs. The location of damage induced by impact hits was visible in the resulting images. The signals could easily be detected even between the most distant transducer pairs, indicating the possibility of monitoring even very large concrete structures.
Embedded sensing for structural health monitoring is a rapidly expanding field, propelled by algorithmic advances in structural health monitoring and the ever-shrinking size and cost of electronic hardware necessary for its implementation. Although commercial systems are available to perform the relevant tasks, they are usually bulky and/or expensive because of their high degree of general utility to a wider range of applications. As a result, multiple separate devices may be required in order to obtain the same results that could be obtained with a structural health monitoring–specific device. This work presents the development and deployment of a versatile, Wireless Active-Sensing Platform, designed for the particular needs of embedded sensing for multi-scale structural health monitoring. The Wireless Active-Sensing Platform combines a conventional data acquisition ability to record voltage output (e.g. from strain or acceleration transducers) with ultrasonic guided wave-based active-sensing, and a seamlessly integrated impedance measurement mode, enabling impedance-based structural health monitoring and piezoelectric sensor diagnostics to reduce the potential for false positives in damage identification. The motivation, capabilities, and hardware design for the Wireless Active-Sensing Platform are reviewed, and three deployment examples are presented, each demonstrating an important aspect of embedded sensing for structural health monitoring.
This study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to an SSP 34-m blade mounted on a test rig. Operational modal analysis was conducted to obtain mode shapes for undamaged and damaged states of the blade. Subsequently, the mode shapes were analyzed with one-dimensional continuous wavelet transformations for damage identification. The basic idea of the method is that structural damage will introduce local mode shape irregularities which are captured in the continuous wavelet transformation by significantly magnified transform coefficients, thus providing combined damage detection, localization, and size assessment. It was found that due to the nature of the proposed method, the value of the identification results highly depends on the number of employed measurement points. Since a limited number of measurement points were utilized in the experiments, only certain damage-sensitive modes, in which pronounced damage-induced mode shape changes occur, are applicable for valid identification of the damage.
In this contribution, first, the results in the development of a structural health monitoring approach for the foundations of an offshore wind turbine based on its resonance frequencies will be presented. Key problems are the operational and environmental variability of the resonance frequencies of the turbine that potentially conceal any structural change. This article uses a (non-)linear regression model to compensate for the environmental variations. An operational case-by-case monitoring strategy is suggested to cope with the dynamic variability between different operational cases of the turbine. Real-life data obtained from an offshore turbine on a monopile foundation are used to validate the presented strategy and to demonstrate the performance of the presented approach. First, the results indicate an overall stiffening of the investigated structure.
The research presented in this article focuses on a 9-m CX-100 wind turbine blade, designed by a team led by Sandia National Laboratories and manufactured by TPI Composites Inc. The key difference between the 9-m blade and baseline CX-100 blades is that this blade contains fabric wave defects of controlled geometry inserted at specified locations along the blade length. The defect blade was tested at the National Wind Technology Center at the National Renewable Energy Laboratory using a schedule of cycles at increasing load level until failure was detected. Researchers used digital image correlation, shearography, acoustic emission, fiber-optic strain sensing, thermal imaging, and piezoelectric sensing as structural health monitoring techniques. This article provides a comparison of the sensing results of these different structural health monitoring approaches to detect the defects and track the resultant damage from the initial fatigue cycle to final failure.
Early detection of leakages in hydraulic infrastructures is important to ensure their safety and security. Significant flow of water through the dike can be an indicator of internal erosion and results in a thermal anomaly. Temperature measurements are therefore capable of revealing information linked to leakage. Optical fiber–based distributed temperature sensors present an economically viable and reliable solution for recording spatio-temporal temperature data over long distances, with spatial and temperature resolutions of 1 m and 0.05°C, respectively. The acquired data are influenced by several factors, among them water leakages, heat transfer through the above soil depth, seasonal thermal variations, and the geomechanical environment. Soil properties such as permeability alter the acquired signal locally. This article presents leakage detection methods based on signal processing of the raw temperature data from optical fiber sensors. The first approach based on source separation identifies leakages by separating them from the non-relevant information. The second approach presents a potential alarm system based on the analysis of daily temperature variations. Successful detection results for simulated as well as real experimental setups of Electricité de France are presented.
A damage detection scheme for multiple crack detection in beams is presented, based on a transfer matrix derived from beam element with two cracks. Based on fracture mechanics principles, a crack is modelled as a hinge, which provides an additional flexibility to the element. Each element is assumed to have two open-edge cracks and a new transfer matrix called two crack transfer matrix is developed using finite element method. Using an inverse approach, the transfer matrix is used to predict cracks in a beam. The state vector at a node includes displacements, forces and moments at that node; when it is multiplied with the transfer matrix, the state vector at the adjacent node can be obtained. The state vector formed at the starting node, known as initial state vector, needs to be estimated, from which state vectors at adjacent nodes are predicted using the transfer matrix. Displacement responses are measured at a few adjacent nodes in the structure. The mean square error between measured and predicted responses is minimized using a heuristic optimization algorithm, with crack depth and location in each element as the optimization variables. Two numerical examples, a cantilever and a sub-structure of a frame with nine members, are solved with two cracks in each element. The damage detection method is also validated experimentally by local identification of sub-structure of a fixed beam where the initial state vector is measured using strain gauges and accelerometers. Using this method, two cracks per single element were successfully identified. The two crack transfer matrix method is suitable for local damage identification in large structures.
Carbon nanotube thread has shown strong promise to be built into or onto composite materials for strain and damage monitoring via the material’s piezoresistive property. It has been found that a distinguishing feature of sensing thread incorporated in these materials is the detection of micro-cracking. This study articulates how embedded carbon nanotube thread in unidirectional glass fiber composites can identify the onset of matrix cracking, track crack growth, and differentiate between crack breathing and closing states. This information is obtained by analyzing the resistance response of the thread with a low-speed data acquisition system and a simple Wheatstone bridge circuit. A digital optical microscope was utilized to verify that a micro-crack was indeed present in the structure at the location of the sensor thread. Additionally, to demonstrate the effectiveness of this crack detection approach compared to past crack detection approaches, a comparison against foil-type strain gauges and piezoelectric accelerometers was made. Finally, a simple crack model is presented for the sensor thread.
An integrated experimental and numerical investigation of fatigue damage diagnosis in composite plates is presented in this study. First, the fatigue testing setup for carbon–carbon composite coupons is described with corresponding health monitoring approach through Lamb wave–based diagnostic data collection. In order to study the effects of degradation evolution, a finite element model is used to simulate the effect on Lamb wave propagation due to fatigue-induced delamination and matrix cracking. Simulation results are compared with the experimental testing to first validate the model and then develop several features as potential damage indicators. A parametric study is conducted on the effects of varying degrees of delamination and matrix cracking on these features. Results from the model simulations are presented along with the data analysis and discussions on the capability and limitations of the approach. Finally, some conclusions are drawn and future work is proposed based on the results obtained so far.
This article presents the WaveFormRevealer—an analytical framework and predictive tool for the simulation of guided Lamb wave propagation and interaction with damage. The theory of inserting damage effects into the analytical model is addressed, including wave transmission, reflection, mode conversion, and nonlinear higher harmonics. The analytical model is coded into MATLAB, and a graphical user interface (WaveFormRevealer graphical user interface) is developed to obtain real-time predictive waveforms for various combinations of sensors, structural properties, and damage. In this article, the main functions of WaveFormRevealer are introduced. Case studies of selective Lamb mode linear and nonlinear interaction with damage are presented. Experimental verifications are carried out. The article finishes with summary and conclusions followed by recommendations for further work.
The performance of guided wave imaging strategies used in Structural Health Monitoring relies on the accurate knowledge of mechanical properties for proper damage detection and localization. In order to increase the performance and robustness of such algorithms, it is desirable to implement autonomous approaches that can characterize the mechanical properties of the structure whatsoever the environmental and operational conditions. This article presents an innovative in situ and integrated characterization procedure based on guided waves that evaluates the thermo-mechanical properties of a structure when subjected to thermal variations prior to imaging using the same set of piezoceramic transducers used for imaging. These properties are then exploited in the damage imaging using a correlation-based algorithm (Excitelet) combined with the optimal baseline subtraction. The characterization strategy uses a genetic algorithm to identify the optimal set of mechanical properties leading to the best correlation between an analytical formulation of dispersed guided waves propagation and experimental measurements. The strategy is assessed experimentally on an aluminum plate with three sparse bonded piezoceramic transducers used for both characterization and imaging at various temperatures, representative of operational conditions of an aircraft. An artificial damage is subsequently introduced in the plate, and the effect of the accuracy of the mechanical properties estimation on imaging is assessed through the detection capability, positioning, accuracy, and correlation amplitude. The approach is then compared to three imaging methods, namely, baseline-free imaging, imaging without considering thermo-mechanical effects, and imaging using stretching methods traditionally used to compensate for temperature effects.
Structural health monitoring has been increasingly used due to the advances in sensing technology and data analysis, facilitating the shift from time-based to condition-based maintenance. This work is part of the efforts which have applied structural health monitoring to the Sydney Harbour Bridge – one of Australia’s iconic structures. It combines dimensionality reduction and pattern recognition techniques to accurately and efficiently distinguish faulty components from well-functioning ones. Specifically, random projection is used for dimensionality reduction on the vibration feature data. Then, healthy and damaged patterns of bridge components are learned in the lower dimensional projected space using supervised and unsupervised machine learning methods, namely, support vector machine and one-class support vector machine. The experimental results using data from a laboratory-based building structure and the Sydney Harbour Bridge showed high feasibility of applying machine learning techniques to dimensionality reduction and damage detection in structural health monitoring. Random projection combined with support vector machine significantly reduces the computational time while maintaining the detection accuracy. The proposed method also outperformed popular dimensionality reduction techniques. The computational time of the method using random projection can be more than 200 times faster than that without using dimensionality reduction while still achieving similar detection accuracy.
This article presents experimental verification on damage identification of a substructure using a wavelet-domain response reconstruction technique. The response reconstruction is based on the unit impulse response function in the wavelet domain to form a transformation matrix between two different sets of time-domain response vectors. The initial finite element model updating is performed to achieve an accurate model in the intact stage as a baseline, and measured acceleration responses from the damaged substructure are used for the damage identification. Substructure damage identification is conducted by minimizing the discrepancy between a measured response vector and the reconstructed one. A dynamic response sensitivity-based model updating method is used for the identification of the target substructure. Local damage is identified as a change in the elemental stiffness factors. The adaptive Tikhonov regularization technique is adopted to improve the identification results with measured responses including measurement and environmental noises in laboratory. Experimental studies on a 7-storey plane frame structure are conducted to investigate the accuracy of the presented response reconstruction technique and the performance of substructure damage identification approach. Good response reconstruction accuracy is obtained with the baseline model, and the introduced damages in the substructure can be identified effectively. The damage locations are identified correctly with a close estimation of damage extents.
A Bayesian approach is proposed to quantitatively identify damages in beam-like structures using experimentally measured guided wave signals. The proposed methodology treats the damage location, length and depth as unknown parameters. Damage identification is achieved by solving an optimization problem, in which a hybrid particle swarm optimization algorithm is applied to maximize the probability density function of a damage scenario conditional on the measured guided wave signals. Signal envelopes extracted by the Hilbert transform are proposed to minimize the complexity of the optimization problem in order to enhance the robustness and computational efficiency of the damage identification. One of the advantages of the proposed methodology is that instead of only pinpointing the multivariate damage characteristics, the uncertainty associated with the damage identification results is also quantified. This outcome provides essential information for making decisions about the remedial work necessary to repair structural damage. The experimental data consist of guided wave signals measured at a single location of the beams. A number of experimental case studies considering damages of different scenarios are used to demonstrate the success of the proposed Bayesian approach in identifying the damages. The results show that the proposed approach is able to accurately identify damages, even when the extent of the damage is small.
Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.
This paper presents a machine-learning-based approach for the structural health monitoring (SHM) of in-situ timber utility poles based on guided wave (GW) propagation. The proposed non-destructive testing method combines a new multi-sensor testing system with advanced statistical signal processing techniques and state-of-the-art machine learning algorithms for the condition assessment of timber utility poles. Currently used pole inspection techniques have critical limitations including the inability to assess the underground section. GW methods, on the other hand, are techniques potentially capable of evaluating non-accessible areas and of detecting internal damage. However, due to the lack of solid understanding on the GW propagation in timber poles, most methods fail to fully interpret wave patterns from field measurements. The proposed method utilises an innovative multi-sensor testing system that captures wave signals along a sensor array and it applies machine learning algorithms to evaluate the soundness of a pole. To validate the new method, it was tested on eight in-situ timber poles. After the testing, the poles were dismembered to determine their actual health states. Various state-of-the-art machine learning algorithms with advanced data pre-processing were applied to classify the poles based on the wave measurements. It was found that using a support vector machine classifier, with the GW signals transformed into autoregressive coefficients, achieved a very promising maximum classification accuracy of 95.7±3.1% using 10-fold cross validation on multiple training and testing instances. Using leave-one-out cross validation, a classification accuracy of 93.3±6.0% for bending wave and 85.7±10.8% for longitudinal wave excitation was achieved.
The propagation properties of ultrasonic waves in rebar-reinforced concrete beams were investigated and their ability for damage identification was demonstrated. Rectangular piezoelectric ceramics were attached at the exposed ends of the rebar to monitor the wave transmission along the rebar with and without simulated corrosion, which was introduced in the form of partial removal of material from the rebar. Experimental testing demonstrated that the presence of concrete had a significant influence on the propagation characteristics of guided waves along the rebar. In consideration of the inevitable discrepancies in different concrete beams due to individual specimen preparation and sensor installation, the time-reversal process was applied to identify the damage. A damage index was defined based on the correlation coefficient between the actuated and the reconstructed wave signals. Wavelet transform was applied to overcome the wave conversion difficulty and to reduce the noise in the captured wave signals. Damage of different sizes was introduced and then was correlated with the damage index. Enlarging the damage size resulted in an increase in the level of distortion in the reconstructed wave signals, and consequently, a higher damage index was obtained. The results demonstrate the efficiency of the time-reversal process in identifying damage in rebar-reinforced concrete structures.
The use of Mahalanobis squared distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis squared distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis squared distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field.
Mode shape analysis by wavelet transform has been used effectively for vibration-based damage detection in plates. As an extension of previous studies, this study focuses on an improved method for damage detection in plates: scrutiny of operating deflection shapes by two-dimensional directional Gaussian wavelet transforms. With this method, the proposed two-dimensional directional Gaussian wavelet can characterize directional information about damage; moreover, the operating deflection shapes can be used to address the real-time dynamic characteristics of a plate. To identify damage, the local surface of the plate is scanned using a scanning laser vibrometer to generate the local operating deflection shape, which is interrogated by two-dimensional directional Gaussian wavelets for damage. The feasibility of the method is numerically demonstrated using a low-magnitude operating deflection shape of a two-sided clamped plate, incorporating white noise with signal-to-noise ratio of 40 dB. The applicability of the method is then experimentally validated by detecting a cross-like notch in a suspended aluminum plate with the operating deflection shapes measured by a scanning laser vibrometer. Numerical and experimental results show that the method is capable of revealing directional features of small damage with high precision and strong robustness against noise. It appears that this damage detection method is related only to the spatially distributed measurement of vibrational responses in local critical regions of the plate. With this local property, the method requires no numerical or physical benchmark models for the entire structure in question nor any prior knowledge of either the material properties or the boundary conditions of the structure. (The Matlab code performing directional Gaussian wavelet transform can be provided by the corresponding author as per request.)
In this article, the comparison of two noncontact measurement methods dedicated to civil engineering structures’ state examination is presented. The vision-based method computes the displacement field of the analyzed structure by means of the digital image correlation coefficient. The system consists of one or more high-resolution digital cameras mounted on a head or on portable tripods. The developed methodology and created software application embedded in an MS-Windows operating system are presented. The second system measures the deflection of the structures by means of a radar interferometer. In both cases, it is possible to measure many points on the structure simultaneously. This article presents a comparison of the displacement field measurement performed on a field setup, as well as the span of a steel bridge designed for tram traffic. Both systems are described, with special attention given to their application in measurements of civil engineering structures. This article demonstrates a preliminary test performed to verify both of the noncontact systems in relation to high-accuracy measurement devices, the precise surveying level, and the electronic dial indicator of displacement. The experiment was designed intentionally to simulate the geometric conditions of the real structure, but the displacement values were generated and controlled by the operator. As a key study, a steel viaduct subjected to an operational load was measured, as a type of structure for which the observation is required in terms of structural health monitoring. It was subjected to the operational load caused by tram traffic. Both examined systems were applied. The accuracy analysis of both systems was investigated, and the obtained results were discussed.