A Two-Stage Model-Based Dynamic Reliability Evaluation Method in Individual Monitoring: A Case Study on Bearing Vibration Data

Traditional degradation-based reliability evaluation methods are typically based on rich data from a population of similar products, providing an average description of product performance. To capture individual characteristics for personalized maintenance, a dynamic reliability evaluation framework is proposed based on the individual monitoring data, which integrates a two-stage scheme and incorporates the physical model. The state-space model is first constructed based on Paris' Law to accurately describe bearing degradation, combining both physical mechanisms and secondary random factors. Then, an online stage division strategy based on an expanding time window is proposed, which implements change point detection and performs parameter estimation to serve as a priori information. Next, degradation state distributions and model parameters are adaptively estimated in the second stage using the extended Kalman filter, and the reliability is evaluated in real time based on the interval failure rate. Finally, to demonstrate the efficacy of the proposed framework, a comparative practical case study on bearing vibration data is presented.