Adaptive Event-Triggered Fault Detection for Interval Type-2 Fuzzy Markov Jump Systems Under Fading Channels

Abstract

This paper focuses on the asynchronous fault detection (FD) problem for interval type-2 fuzzy Markov jump systems under bandwidth constraints and fading channels. A novel adaptive event-triggered scheme (AETS) is introduced, which not only saves communication resources but also guarantees a minimum data transfer rate. Based on the released date under the trigger protocol, a fuzzy asynchronous FD filter is designed, where the network-induced transmission delays and fading measurements are both considered. Then, using the residual signals generated by the filter, a generalized detection strategy for actuator faults is proposed. By applying the information about membership functions and the Lyapunov functional approach, the design conditions of the filter are obtained that ensure the stochastic stability and strict dissipativity of the dynamic error system. Compared with the existing methods, the proposed AETS is more capable of reducing the data release rate and avoiding some maltriggering events aroused by disturbance and noise. Finally, a simulation example is provided to validate the effectiveness of the proposed approach. Note to Practitioners—Fault detection has a crucial role in the modern production environment, which provides solutions for equipment health monitoring and fault prevention and has a positive impact on the efficiency and safety of industrial production. Considering the bandwidth constraints and fading channels in practical engineering, an adaptive event-triggered-based fault detection method is proposed in this paper. The proposed event-triggered strategy can effectively save communication resources on the premise of ensuring system performance. Besides, the delay information and fading measurements are fully utilized in the design of the filter, which is more suitable for practical scenarios. The proposed fault detection method can provide operators with early warning of faults for abnormal conditions to avoid more serious damages.