Methods for fault diagnosability analysis of a class of affine nonlinear systems
type: Item
instance of: scholarly article
title: Methods for fault diagnosability analysis of a class of affine nonlinear systems
zbMATH Open document ID: 1394.93039
DOI: 10.1155/2015/409184
author: Xiafu Peng
author: Lixiong Lin
author: Xunyu Zhong
author: Chengrui Liu
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: The fault diagnosability analysis for a given model, before developing a diagnosis algorithm, can be used to answer questions like ``can the fault \(f_i\) be detected by observed states?'' and ``can it separate fault \(f_i\) from fault \(f_j\) by observed states?'' If not, we should redesign the sensor placement. This paper deals with the problem of the evaluation of detectability and separability for the diagnosability analysis of affine nonlinear system. First, we used differential geometry theory to analyze the nonlinear system and proposed new detectability criterion and separability criterion. Second, the related matrix between the faults and outputs of the system and the fault separable matrix are designed for quantitative fault diagnosability calculation and fault separability calculation, respectively. Finally, we illustrate our approach to exemplify how to analyze diagnosability by a certain nonlinear system example, and the experiment results indicate the effectiveness of the fault evaluation methods.
Mathematics Subject Classification ID: 93B07
zbMATH DE Number: 6926286
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/409184
OpenAlex ID: W2021718260
Wikidata QID: Q59118501
cites work: Sensor placement for fault isolation in linear differential-algebraic systems
cites work: Diagnosability analysis of hybrid systems cast in a discrete-event framework
cites work: On fault detectability and isolability
cites work: A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
cites work: A geometric approach to nonlinear fault detection and isolation
description: scientific article; zbMATH DE number 6926286