A multi-source data fusion anomaly detection method for new energy vehicle bearings based on electric drive-adaptive heterogeneous feature collaborative learning
Journal of Vibration and Control
Published online on April 02, 2026
Abstract
Journal of Vibration and Control, Ahead of Print.
Bearings in new energy vehicles (NEVs) are critical components in drive systems, directly affecting vehicle safety, energy efficiency, and operational reliability. To address the challenges of incomplete single-source data and heterogeneous data fusion in ...
Bearings in new energy vehicles (NEVs) are critical components in drive systems, directly affecting vehicle safety, energy efficiency, and operational reliability. To address the challenges of incomplete single-source data and heterogeneous data fusion in ...