Fuzzy fault-detection filtering for uncertain stochastic time-delay systems with randomly missing data

This paper is concerned with the robust H∞ fault-detection problem for a class of uncertain discrete stochastic Takagi–Sugeno fuzzy systems with time-varying delays, parameter uncertainties and randomly missing data in a network environment. We deal with the fault detection by designing fuzzy-rule-independent and fuzzy-rule-dependent fault detection filters, which guarantee the fault detection system is not only robustly stochastically stable, but also satisfies a prescribed H∞ performance level for all admissible uncertainties. Lyapunov stability theory and the linear matrix inequality technique are utilized to derive novel conditions for the desired fault detection filters. The residual evaluation function and detection threshold are also discussed in order to detect the occurrence of faults. A numerical example and a nonlinear mass-spring-damper mechanical system are provided to demonstrate that our fault-detection system is sensitive to faults and simultaneously robust to disturbances.