Interpretable temporal degradation state chain based fusion graph for intelligent bearing fault detection

Machine fault detection is a crucial task in prognostics health management (PHM), while most of the data-driven methods lack model interpretability and transparency. Aiming at promoting reliability, transparency, and anomaly detection performance, an interpretable temporal degradation state chain based fusion graph model is proposed to realize intelligent fault detection in this study. Based on fusion direction distance (FDD), a life-cycle fusion graph is first constructed with a temporal degradation state chain of vibration data. Along with preserving time information, the fusion graph can also comply with the irreversible degradation principle. Once the fusion graph is well developed, the degeneration track of the test sample can be located in the graph and the health status can be predicted, according to a mapping rule and a label determination rule developed in this paper. The anomaly detection accuracy achieves more than 97.8% on XJTU-SY bearing datasets and 92.8% on a NASA bearing dataset. Except for normal and abnormal states, the transition state can also be monitored by the proposed framework, in which incipient faults are initiated. Besides, a hyper-parameter selection method is generated based on Silhouette Coefficient. Moreover, experimental studies have shown the superiority, interpretability, and visibility of this proposed method.