An overview of recent advances in model-based event-triggered fault detection and estimation. Issue 4 (12th March 2023)

• Record Type: Journal Article
• Authors: Zhong, Maiying; Zhu, Xiaoqiang; Xue, Ting; Zhang, Lu
• Is Part Of: International journal of systems science. Volume 54:Issue 4(2023)
• Page Start: 929
• Page End: 943
• Publication Date: 2023-03-12
• Subjects: Event-triggered mechanism -- fault detection -- residual generator -- fault estimation -- H∞ filtering -- Hi/H∞ optimisation approach; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2022.2146990
• Languages: English
• ISSNs: 0020-7721

Abstract

ABSTRACT: Event-triggered fault diagnosis has attracted tremendous research attention in the last decade due to its superiority in improving the utilisation efficiency of communication resources. Different from traditional works of time-driven, event-triggered schemes are used to determine whether the current measurement output should be released to the fault detection filter, while the sensor data not satisfying a predefined triggering condition will be discarded directly. As such, research on event-triggered fault diagnosis has been a challenging issue and many outstanding results have been reported. This paper presents a survey of model-based event-triggered fault detection (FD) and fault estimation (FE) methods mainly based on the techniques of residual generation. First, an overview of recent advances in state estimation-based methods of event-triggered FD is provided, which include the event-triggered FD for dynamic systems subject to Gaussian noises, the H ∞ filtering formulation of event-triggered FD, and the event-triggered H i / H ∞ optimisation-based FD. Second, the representative results of parity space-based event-triggered FD are reviewed. Third, recent results on event-triggered FE are also reviewed. Finally, several challenging issues on event-triggered fault diagnosis are provided for future research.