Dynamic Reliability Assessment of Hierarchical Multistate Systems With Sensors’ Degradation

Abstract

Engineered systems are increasingly integrating sensor techniques to trace their specific degradation behaviors, so as to facilitate their dynamic reliability assessment. Due to the hierarchical structure of these systems, sensing data can be collected at multiple physical levels, including the entire system, subsystems, and components. The quality of collected multilevel sensing data, however, decreases inevitably with the degradation of sensors mounted within each system, leading to a declining trustworthiness of dynamic reliability assessment for each specific individual system. This article develops a new dynamic reliability assessment framework of hierarchical multistate systems suffering from sensors’ degradation. The proposed framework mainly contains three steps: 1) utilizing discrete-state and continuous-state stochastic processes to, respectively, model the degradation behaviors of two types of sensors; 2) integrating these two types of sensors’ degradation models to update the joint state probability distribution of both the monitored objects and sensors by fusing multilevel sensing data; 3) deriving the marginal state probability distribution of the entire system to dynamically assess system reliability. A three-component system and an electromechanical actuator system in landing gear systems are exemplified to illustrate the performance of the proposed method.