Data-driven fault detection for Lipschitz nonlinear systems: From open to closed-loop systems

被引：4

作者：

Chen, Hongtian ^{[1
,3
]}

Liu, Zhongyan ^{[2
]}

Huang, Biao ^{[3
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China

[2] Univ Illinois, Dept Stat, Urbana, IL 61801 USA

[3] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 1H9, Canada

来源：

AUTOMATICA | 2023年 / 155卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

Data-driven designs; Nonlinear systems; Parameter identification; Fault detection (FD); Piecewise function; OBSERVER; DIAGNOSIS; ACTUATOR; DESIGN;

D O I：

10.1016/j.automatica.2023.111161

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a novel data-driven fault detection (FD) method for Lipschitz nonlinear systems. The proposed method is developed by considering that the sample size of training data is limited, while the global system nonlinearity is taken into account. It is a nonparametric approach and consists of two FD versions corresponding to open-loop and closed-loop systems, respectively. It achieves a tradeoff between approximation and estimation errors. By quantifying the unknown modeling error that is closely related to the threshold used in FD tasks, an upper bound is obtained so that trial-and-error for finding the threshold can be avoided. The effectiveness of the proposed data-driven schemes is illustrated by two simulation studies. & COPY; 2023 Published by Elsevier Ltd.

引用

页数：9

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