Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis
Joint Authors
Source
Journal of Control Science and Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-20
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Electronic engineering
Information Technology and Computer Science
Abstract EN
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.
And, 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.
American Psychological Association (APA)
Xu, Xiaoming& Wen, Chenglin. 2017. Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis. Journal of Control Science and Engineering،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1173422
Modern Language Association (MLA)
Xu, Xiaoming& Wen, Chenglin. Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis. Journal of Control Science and Engineering No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1173422
American Medical Association (AMA)
Xu, Xiaoming& Wen, Chenglin. Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis. Journal of Control Science and Engineering. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1173422
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173422