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Multi-layer contribution propagation analysis for fault diagnosis

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Title: Multi-layer contribution propagation analysis for fault diagnosis
Authors: Tan, R
Cao, Y
Item Type: Journal Article
Abstract: The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multilayer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multilayer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study (Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multilayer linear algorithms.
Issue Date: 1-Feb-2019
Date of Acceptance: 4-Jun-2018
URI: http://hdl.handle.net/10044/1/65221
DOI: https://dx.doi.org/10.1007/s11633-018-1142-y
ISSN: 1476-8186
Publisher: Springer
Start Page: 40
End Page: 51
Journal / Book Title: International Journal of Automation and Computing
Volume: 16
Issue: 1
Copyright Statement: © 2018 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 675215
Keywords: Science & Technology
Automation & Control Systems
Process monitoring
fault detection and diagnosis
contribution plots
feature extraction
multivariate statistics
Industrial Engineering & Automation
Publication Status: Published
Open Access location: https://link.springer.com/article/10.1007/s11633-018-1142-y?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst&utm_source=ArticleAuthorOnlineFirst&utm_medium=email&utm_content=AA_en_06082018&ArticleAuthorOnlineFirst_20180930#citeas
Online Publication Date: 2018-09-27
Appears in Collections:Faculty of Engineering
Chemical Engineering