Fault detection and diagnosis of relative position detection sensor for high speed maglev train based on kernel principal component analysis

2019 ◽  
Author(s):  
Chunhui Dai ◽  
Peng Deng ◽  
Zhiqiang Long
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Relative Position ◽  
High Speed ◽  
Principal Component ◽  
Fault Detection And Diagnosis ◽  
Kernel Principal Component Analysis ◽  
Maglev Train ◽  
Position Detection ◽  
Detection And Diagnosis

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

Fault Detection and Diagnosis for Sensor in Complex Control System Based on KPCA

2014 ◽  
Vol 623 ◽  
pp. 202-210
Author(s):  
Ping Xu ◽  
You Cai Wang ◽  
Kai Wang ◽  
Qiu Yan Wang
Keyword(s):  
Control System ◽  
Fault Detection ◽  
Principal Component ◽  
Kernel Functions ◽  
Fault Detection And Diagnosis ◽  
Kernel Principal Component Analysis ◽  
Practical Result ◽  
Complex Control ◽  
Complex Control System ◽  
Detection And Diagnosis

The Fault detection and diagnosis for sensors are important for the performance of the complex control system seriously. The kernel principal component analysis (KPCA) effectively captures the nonlinear relationship of the process variables, which computes principal component in high-dimensional feature space by means of integral operators and nonlinear kernel functions. The KPCA method is used in diagnosing for four common sensor faults. At first its fault is detected by Q statistic; secondly its fault is identified by T2 contribution percent change. The simulation and the practical result show the KPCA method has good performance on complex control system in sensor fault detection and diagnosis.

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