Fault diagnosis method based on information entropy and relative principal component analysis
type: Item
instance of: scholarly article
title: Fault diagnosis method based on information entropy and relative principal component analysis
zbMATH Open document ID: 1400.93083
DOI: 10.1155/2017/2697297
author: Chenglin Wen
author: Xiaoming Xu
published in: Journal of Control Science and Engineering
publication date: 2018-10-15T00:00:00Z
review text: Summary: In traditional Principle Component Analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected Principal Components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable's dimension in the dataset. Then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.
Mathematics Subject Classification ID: 93B40
Mathematics Subject Classification ID: 62K25
Mathematics Subject Classification ID: 94A17
Mathematics Subject Classification ID: 15A18
zbMATH DE Number: 6954241
zbMATH Keywords: principle component analysis
zbMATH Keywords: fault diagnosis
zbMATH Keywords: information entropy
Wikidata QID: Q59145585
describes a project that uses: UCI-ml
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/2697297
OpenAlex ID: W2589738557
cites work: Relative PCA with Applications of Data Compression and Fault Diagnosis
cites work: A Mathematical Theory of Communication
DBLP publication ID: journals/jocsae/XuW17
description: scientific article; zbMATH DE number 6954241