Robust Sensor Fault Detection and Estimation for Parabolic Distributed Parameter Systems

Sensors play a key role in monitoring distributed parameter systems (DPSs), such as thermal and chemical diffusion-reaction processes. However, sensor faults can result in data distortion, degraded system performance, and even catastrophic consequences. This article proposes a model-based sensor fau...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 74; S. 1 - 10
Hauptverfasser:	Wang, Kui, Zhou, Jinhui, Shen, Wenjing, Zhou, Yu, Wei, Peng, Mou, Xiaolin, Chen, Liqun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Chemical diffusion Chemicals Detection distributed parameter system (DPS) Distributed parameter systems disturbance Electronic mail Estimation Fault detection Fault diagnosis Fault tolerant systems Information systems Interference Linear matrix inequalities Mathematical models Noise Observers sensor fault Training
ISSN:	0018-9456, 1557-9662
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Abstract	Sensors play a key role in monitoring distributed parameter systems (DPSs), such as thermal and chemical diffusion-reaction processes. However, sensor faults can result in data distortion, degraded system performance, and even catastrophic consequences. This article proposes a model-based sensor fault diagnosis framework for DPSs, which can effectively detect the fault time and estimate the fault intensity. The proposed method only requires the limited measured output without any state information. Besides, noise will cause perturbations in the sampling data. Correspondingly, a linear matrix inequality (LMI) considering disturbance is designed. Through theoretical analysis, the convergence of fault estimation error is ensured. The effectiveness of the proposed method is verified on a heat transfer rod and a chemical diffusion-reaction system with static and time-varying sensor faults under disturbances. The root mean square errors of all sensor fault estimates are within 0.1078.
AbstractList	Sensors play a key role in monitoring distributed parameter systems (DPSs), such as thermal and chemical diffusion-reaction processes. However, sensor faults can result in data distortion, degraded system performance, and even catastrophic consequences. This article proposes a model-based sensor fault diagnosis framework for DPSs, which can effectively detect the fault time and estimate the fault intensity. The proposed method only requires the limited measured output without any state information. Besides, noise will cause perturbations in the sampling data. Correspondingly, a linear matrix inequality (LMI) considering disturbance is designed. Through theoretical analysis, the convergence of fault estimation error is ensured. The effectiveness of the proposed method is verified on a heat transfer rod and a chemical diffusion-reaction system with static and time-varying sensor faults under disturbances. The root mean square errors of all sensor fault estimates are within 0.1078.
Author	Chen, Liqun Zhou, Yu Mou, Xiaolin Wang, Kui Shen, Wenjing Wei, Peng Zhou, Jinhui
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SubjectTerms	Chemical diffusion Chemicals Detection distributed parameter system (DPS) Distributed parameter systems disturbance Electronic mail Estimation Fault detection Fault diagnosis Fault tolerant systems Information systems Interference Linear matrix inequalities Mathematical models Noise Observers sensor fault Training
Title	Robust Sensor Fault Detection and Estimation for Parabolic Distributed Parameter Systems
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