A TE Process Fault Diagnosis Method Based on Improved Wavelet Threshold Denoising and Principal Component Analysis

2018 ◽  
Author(s):  
Xingang Wang ◽  
Chengwen Zhuang
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnosis Method ◽  
Process Fault Diagnosis

scholarly journals Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zihan Wang ◽  
Chenglin Wen ◽  
Xiaoming Xu ◽  
Siyu Ji
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Dimensional Space ◽  
Principal Component ◽  
Data Preprocessing ◽  
Component Analysis ◽  
Gap Metric ◽  
Preprocessing Method ◽  
Original Dataset ◽  
Diagnosis Method

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.

Fault Diagnosis Method of Offset Printer Feeding Mechanism Based on Kernel Principal Component Analysis and K-Means Clustering

2015 ◽  
Vol 731 ◽  
pp. 395-400 ◽  
Author(s):  
Qian Qian Xu ◽  
Hai Yan Zhang ◽  
He Ping Hou ◽  
Zhuo Fei Xu
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
High Speed ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Kernel Principal Component Analysis ◽  
Diagnosis Method ◽  
Detection And Diagnosis

The printing machine is a sort of large-scale equipment characterized by high speed and precision. A fault diagnosis method based on kernel principal component analysis (KPCA) and K-means clustering is developed to classify the types of feeding fault. The multidimensional and nonlinear data of printed image could be reduced by KPCA to make up the deficiency of the traditional K-means clustering method. In this paper, it is experimentally verified that the classification accuracy of the combined method is higher than the traditional clustering analysis method in feeding fault detection and diagnosis. This method provides a shortcut for the determination of fault sources and realizes multi-faults diagnosis of printing machinery efficiently

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