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Journal of Control Science and Engineering
Volume 2017, Article ID 2697297, 8 pages
Research Article

Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

Correspondence should be addressed to Chenglin Wen; nc.ude.udh@lcnew

Received 19 December 2016; Accepted 22 January 2017; Published 20 February 2017

Academic Editor: Xiao He

Copyright © 2017 Xiaoming Xu and Chenglin Wen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.