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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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 10
Main Authors: Wang, Kui, Zhou, Jinhui, Shen, Wenjing, Zhou, Yu, Wei, Peng, Mou, Xiaolin, Chen, Liqun
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Description
Summary: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.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2025.3548791