Reliability assessment for partially monitored systems based on degradation hidden Markov models with time-varying parameters

With the rapid advancement of sensing technology, some critical components within engineering systems are equipped with sensors to collect condition monitoring (CM) signals. Such systems are referred to as partially monitored systems because only selected components are monitored. However, the method to integrate real-time component-level CM signals into reliability assessments of these systems remains unexplored. This study introduces a novel reliability assessment method designed to address the challenges of evaluating the reliability of partially monitored systems, particularly considering the highly non-stationary nature of CM signals and their dependence on the component states. A multi-state degradation hidden Markov model with time-varying parameters (DHMM-TVP) is developed to better handle the non-stationary and non-linear nature of CM signals. The expectation-maximization (EM) algorithm is adapted to estimate the unknown parameters within the DHMM-TVP framework. Furthermore, leveraging DHMM-TVP in combination with a functional kernel regression model, a generalized reliability assessment method is proposed, specifically tailored for cases where the system reliability structure is unknown or only partially known. A numerical simulation study and two case studies were conducted to validate the proposed reliability assessment approach. The component-level validation was performed using an experimental bearing accelerated degradation
testing dataset, while the system-level verification employed aircraft turbofan engine datasets from the NASA prognostics data repository, collectively demonstrating the effectiveness of the proposed method.