Real-Time Reliability Assessment of Multivariable Systems With Multiple Failure Modes

Reliability analysis is essential to guide maintenance strategies in structural health monitoring of complex systems that have multiple variables and suffer from multiple failure modes. However, most existing prognostic approaches are based on sufficient data from a class of systems, and focus on univariate systems with one failure mode. This article proposes a novel approach to address this problem. Individual monitoring data is combined with physical information to recursively estimate the performance states of the system using the extended Kalman filter. The correlation among multiple variables is characterized by constructing multivariate distributions derived from estimation results. In addition, the impact of the correlation between multiple failure modes on system reliability is comprehensively investigated through the formulation of multidimensional functional variables and receptive fields. The failure rate is then derived to achieve real-time evaluation of system reliability. Furthermore, the distribution of future performance states is predicted considering multisource uncertainty propagation, and system reliability is predicted using Bayes' theorem. Finally, a comparative case study concerning a liquid-level control system is presented to demonstrate the effectiveness of the proposed technique in reliability evaluation and prediction.