Fault diagnosis method based on gap metric data preprocessing and principal component analysis
type: Item
instance of: scholarly article
title: Fault diagnosis method based on gap metric data preprocessing and principal component analysis
zbMATH Open document ID: 1403.93032
DOI: 10.1155/2018/1025353
author: Zihan Wang
author: Chenglin Wen
author: Siyu Ji
author: Xiaoming Xu
published in: Journal of Control Science and Engineering
publication date: 2018-12-12T00:00:00Z
review text: Summary: Principal Component Analysis (PCA) is widely used in fault diagnosis. Because the traditional data preprocessing method ignores the correlation between different variables in the system, the feature extraction is not accurate. In order to solve it, this paper proposes a kind of data preprocessing method based on the gap metric to improve the performance of PCA in fault diagnosis. For different types of faults, the original dataset transformation through gap metric can reflect the correlation of different variables of the system in high-dimensional space, so as to model more accurately. Finally, the feasibility and effectiveness of the proposed method are verified through simulation.
Mathematics Subject Classification ID: 93A15
Mathematics Subject Classification ID: 62P30
zbMATH DE Number: 6992111
zbMATH Keywords: gap metric data
zbMATH Keywords: principal component analysis
zbMATH Keywords: fault diagnosis
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/1025353
OpenAlex ID: W2803211482
cites work: Relative PCA with Applications of Data Compression and Fault Diagnosis
cites work: Fault diagnosis method based on information entropy and relative principal component analysis
cites work: The gap metric: Robustness of stabilization of feedback systems
cites work: Optimal robustness in the gap metric
cites work: Frequency domain uncertainty and the graph topology
Wikidata QID: Q129807636
description: scientific article; zbMATH DE number 6992111