A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem

• Published in: IEEE transactions on industrial informatics Vol. 12; no. 3; pp. 924 - 932
• Main Authors: Wang, Yu, Peng, Yizhen, Zi, Yanyang, Jin, Xiaohang, Tsui, Kwok-Leung
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bearing; Degradation; Deviation; Estimation; Feature extraction; Health; Kalman filter; Kalman filters; Mathematical models; Prognostics and health management; RUL estimation; Statistics; Strategy; Time-domain analysis; Vibration; Vibrations; Degradation; remaining useful life (RUL) estimation; Kalman filter (KF); prognostics
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2016.2535368

Prognostics of the remaining useful life (RUL) has emerged as a critical technique for ensuring the safety, availability, and efficiency of a complex system. To gain a better prognostic result, degradation information is quite useful because it can reflect the health status of a system. However, due to the lack of accurate information about the plants' degradation, the prognostic model is usually not well established. To solve this problem, this paper proposes a two-stage strategy that is in the context of data-driven modeling to predict the future health status of a bearing, where the degradation information was estimated by calculating the deviation of multiple statistics of vibration signals of a bearing from a known healthy state. Then, a prediction stage based on an enhanced Kalman filter and an expectation-maximization algorithm were used to estimate the RUL of the bearing adaptively. To verify the effectiveness of the proposed approach, a real-bearing degradation problem was implemented.