Least-Squares Fault Detection and Diagnosis for Networked Sensing Systems Using a Direct State Estimation Approach

In this paper, the problems of fault detection, isolation, and estimation are considered for a class of discrete time-varying networked sensing systems with incomplete measurements. A unified measurement model is utilized to simultaneously characterize both the phenomena of multiple communication delays and data missing. A least-squares filter that minimizes the estimation variance is first designed for the addressed time-varying networked sensing systems, and then a novel residual matching (RM) approach is developed to isolate and estimate the fault once it is detected. The RM strategy is implemented via a series of Kalman filters, where each filter is designed to estimate the augmented signal composed of the system state and a specific fault signal. The design scheme for each filter is proposed in a recursive way. The main idea for the fault detection and estimation is that the Kalman filter with least residual value is regarded as corresponding to the right fault signal, and its estimation is utilized to represent the actual occurred fault. The effectiveness of our proposed method is demonstrated via simulation experiments on a real Internet-based three-tank system.