This paper discusses adaptive event-triggered fault detection filter design for fuzzy stochastic models with missing measurements. First, a novel event-triggered strategy is introduced, while an adaptive law is provided to adjust communication threshold dynamically. Compared with traditional event-triggered methods with fixed threshold, the proposed strategy is more effective on saving network communication resources. Second, a Bernoulli stochastic process is proposed to describe the measurement missing phenomenon, which always appears in real network environment. Then, an integrated fault detection model for fuzzy stochastic systems is constructed by taking network-induced delays, adaptive event-triggered strategy and missing measurements into account. A new method is provided to achieve mean-square asymptotical stability of residual model with one desired fault detection objective. Finally, simulation cases are introduced to verify the validity of the designed strategy.

中文翻译：

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具有缺失测量值的模糊随机系统的自适应事件触发故障检测

本文讨论了具有缺失测量值的模糊随机模型的自适应事件触发故障检测滤波器设计。首先，引入了一种新颖的事件触发策略，同时提供了自适应律来动态调整通信阈值。与传统的固定阈值的事件触发方法相比，所提出的策略更有效地节省了网络通信资源。其次，提出了伯努利随机过程来描述真实网络环境中经常出现的测量缺失现象。然后，通过考虑网络引起的延迟、自适应事件触发策略和缺失测量，构建模糊随机系统的集成故障检测模型。提供了一种新方法来实现具有一个期望故障检测目标的残差模型的均方渐近稳定性。最后，通过仿真案例验证了所设计策略的有效性。