Adaptive Memory-Based Event-Triggered Fault Detection for Networked Stochastic Systems

This brief concerns the event-triggered fault detection problem for a class of networked nonlinear stochastic systems with randomly occurring dropouts and time-varying delays to guarantee the safety and reliability of systems. In order to save limited communication bandwidth, a novel adaptive memory-based event-triggered scheme (ETS) is developed, based on which a memory-based fault detection filter (FDF) is constructed to generate residual signal. Then, an event-triggered fault detection framework is established, which is mainly composed of stochastic system, ETS, FDF, fault weighting, and residual evaluation. With considering the ETS, random dropouts, and time-varying transmission delays, the criterion of mean-square asymptotic stability and $H_{\infty }$ performance is given such that the residual signal is sensitive to faults while robust against disturbance. Then, the co-design method of memory-based ETS and FDF is proposed. Finally, a simulation case is provided to demonstrate the effectiveness and advantages of the proposed method.