Automated labeling and abnormal detection based on kernel cluster local outlier factor for machinery health monitoring
Structural Health Monitoring: An International Journal
Published online on March 27, 2026
Abstract
Structural Health Monitoring, Ahead of Print.
Machinery label data is necessary for training intelligent fault diagnosis models. However, unlabeled and abnormal data are commonly seen in these data, resulting in the reduction of data quality. As a result, these low-quality data may lead to inaccurate ...
Machinery label data is necessary for training intelligent fault diagnosis models. However, unlabeled and abnormal data are commonly seen in these data, resulting in the reduction of data quality. As a result, these low-quality data may lead to inaccurate ...