An overview of recent advances in model-based event-triggered fault detection and estimation

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 $H_{\mathrm{\infty }}$ filtering formulation of event-triggered FD, and the event-triggered $H_i/H_{\mathrm{\infty }}$ 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.

KEYWORDS:

• Event-triggered mechanism
• fault detection
• residual generator
• fault estimation
• $H_{\mathrm{\infty }}$ filtering
• $H_i/H_{\mathrm{\infty }}$ optimisation approach

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under [grants number 62233012, 61873149 and 61733009], the Research Fund for the Taishan Scholar Project of Shandong Province of China.

Notes on contributors

Maiying Zhong

Maiying Zhong received her Ph.D. degree in control theory and control engineering from the Northestern University, China, in 1999. From 2000 to 2001, she was a visiting Scholar at the University of Applied Sciences Lausitz, Senftenberg, Germany. From 2002 to July 2008, she was a professor of the School of Control Science and Engineering at Shandong University, Jinan, China. From 2006 to 2007, she was a Postdoctoral Researcher Fellow with the Central Queensland University, Rockhampton, Australia. From 2009 to 2016, she was a professor of Beihang University, Beijing, China. In March 2016, she joined Shandong University of Science and Technology, Qingdao, China, where she is currently a professor with the College of Electrical Engineering and Automation. Her research interests are model based fault diagnosis, fault tolerant systems and their applications.

Xiaoqiang Zhu

Xiaoqiang Zhu received the B.E. degree in the College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China, in 2016. He is currently pursuing the Ph.D. degree from the College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China. His current research interests include the theory and applications of fault diagnosis and fault tolerant control.

Ting Xue

Ting Xue received her M.S. degree in instrumentation science and technology from Beihang University, Beijing, China, in 2016, and the Ph.D. degree in electrical engineering and information technology from the University of Duisburg-Essen, Duisburg, Germany, in 2020. She is currently a Postdoctoral Researcher with the Shandong University of Science and Technology, Qingdao, China. Her research interests include model-based and data-driven fault diagnosis and their applications.

Lu Zhang

Lu Zhang received the Ph.D. degree in Faculty of Information Technology from Beijing University of Technology in 2020. From 2020 to July 2022, she was a post-doctoral with Shandong University of Science and Technology, Shandong, Chin. In August 2022, she joined Shandong University of Science and Technology, where she is currently a lecture with the College of Electrical Engineering and Automation. Her research interests include intelligent fault detection and diagnosis in wastewater treatment process.