Online incipient fault diagnosis based on Kullback-Leibler divergence and recursive principle component analysis

2017 ◽  
Vol 96 (2) ◽  
pp. 426-433 ◽  
Author(s):  
Yi Chai ◽  
Songbing Tao ◽  
Wanbiao Mao ◽  
Ke Zhang ◽  
Zhiqin Zhu
Keyword(s):  
Fault Diagnosis ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Principle Component ◽  
Leibler Divergence ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

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

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoming Xu ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Principal Components ◽  
Information Entropy ◽  
Principle Component Analysis ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Original Dataset ◽  
Diagnosis Method

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.

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