Adaptive Memory-Event-Triggered Fault Detection for Interval Type-2 Fuzzy Markov Jump Systems With Sensor Saturation

This paper investigates the event-triggered asynchronous fault detection (FD) problem for discrete-time interval type-2 (IT2) fuzzy nonhomogeneous higher-level Markov jump systems (MJSs) with sensor saturation. To save network resources, a novel adaptive memory triggering condition based on historical data is introduced. Using the hidden Markov Model and past released packets, an IT2 fuzzy asynchronous FD filter is designed to generate residual signals and detect faults. Based on Lyapunov theory, it is proved that the proposed FD filter ensures the stochastic stability and strict dissipativity of the error dynamic system. Finally, a simulation example is presented to illustrate the effectiveness of the proposed FD method, which shows that the proposed memory-event-triggered-based filter achieves better FD performance than the existing memoryless ones.