Sensor Fault Detection and Diagnosis of Linear Parabolic PDE Systems With Unknown Inputs

• 成果类型: Article
• 署名作者: Cao, Fangfei; Jia, Fanlin; He, Xiao
• 署名单位: Beijing Institute of Technology; Tsinghua University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3276773
• 发表日期: 2024
• 页码: 1014-1021
• 关键词: sigma-modification neural networks; Adaptive observer; parabolic partial differential equations (PDEs); sensor fault detection and diagnosis (FDD)

摘要

This article investigates the sensor bias fault detection and diagnosis problem for linear parabolic partial differential equation (PDE) systems under the existence of unknown input signals. A variation of Wirtinger's inequality is used to design a Luenberger-type PDE observer and radial basis function (RBF) neural networks are applied to approximate the unknown inputs, guaranteeing the boundedness of the state estimation error. Taking the measurement error as the fault indication signal, a residual evaluation logic is developed to achieve fault detection. An adaptive diagnostic law is constructed to estimate the values of sensor bias faults once they are detected. $\sigma$-modification RBF neural networks are designed to compensate for the unknown inputs in the presence of sensor faults, ensuring that both state and sensor bias faults estimation errors converge to some adjustable sets. The effectiveness and applicability of the developed fault diagnosis strategy are demonstrated by simulation results on a heat diffusion process.