Rolling Bearing Fault Detection Using Domain Adaptation Based Anomaly Detection

Rolling bearings play an important role in rotating machinery. According to statistics, rolling bearings cause one-third faults of rotating machinery. Once a rolling bearing malfunctions, it may induce maintenance, affect work efficiency, or even cause the entire equipment to malfunction. Therefore, accurately determining the operating status of bearings is of great significance for maintaining the health of the rotating machinery. Most current fault detections of rolling bearing works focus on traditional anomaly detection models which assume the training set to follow the same distribution of test set. This assumption does not hold in fault detection of rolling bearings across different conditions and traditional anomaly detection models may be invalid. This paper introduces domain adaptation anomaly detection (DAAD) in the fault detection of rolling bearings to address this issue. DAAD can adapt anomaly detection across different distributions. The experiments of rolling bearing fault detection under single condition or across different condition show that DAAD is superior to most of the traditional anomaly detection models.