Bearing Fault Degradation Modeling Based on Multitime Windows Fusion Unsupervised Health Indicator

Most health indicators (HIs) used to predict bearing performance degradation are calculated through supervised learning of failure data. However, due to the limited availability of failure data during the operation of bearings and the uncertainty of failure occurrences, using a supervised learning method to predict failure thresholds of bearings becomes challenging. In this study, a bearing degradation model is proposed through the development of HIs using unsupervised learning. The proposed method extracts features from data with high signal amplitudes among multitime windows data through frequency analysis and develops an HI by fusing these features using principal component analysis (PCA). The six-sigma value for the bearing's HI data is used as a threshold for determining the first predicted time to failure (FPTF), which is a criterion for an unsupervised method of detecting abnormalities in the performance bearings in real time. The proposed method accurately detects abnormal states, and the degradation model shows improved trendability compared to existing models.