Fault Diagnosis of Grounding Grid Based on Principal Component Analysis and Fuzzy Clustering

2013 ◽  
Vol 787 ◽  
pp. 881-885
Author(s):  
Ke Xin Zhao ◽  
Min Fang Peng ◽  
Hu Tan ◽  
Shu Di He ◽  
Mei E Shen ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Fuzzy Clustering ◽  
Fault Location ◽  
Principal Component ◽  
Component Analysis ◽  
Test Point ◽  
Cluster Center ◽  
Economic Losses ◽  
Grounding Grid

The problem of grounding grid fault causes great economic losses, so accurate and efficient fault location is becoming more important. This paper puts forward a new method of fault diagnosis for grounding network.Taking voltage values of test point as fault characteristics and making use of principal component analysis extract fault features from training and test samples, which can eliminate the correlation between the fault symptoms.Taking fuzzy clustering for the samples after feature extraction can get clustering center. By testing sample membership of each sample and each cluster center can diagnose the fault. The outcomes verify that utilizing principal component analysis and fuzzy clustering to solving the fault location of grounding network has good diagnostic effectiveness and efficiency.

scholarly journals Fault Diagnosis for Constant Deceleration Braking System of Mine Hoist based on Principal Component Analysis and SVM

2017 ◽  
Vol 128 ◽  
pp. 05015
Author(s):  
Juan-Juan Li ◽  
Liang Hu ◽  
Guo-Ying Meng ◽  
Guang-Ming Xie ◽  
Ai-Ming Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Component Analysis ◽  
Braking System ◽  
Mine Hoist

scholarly journals COMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS

Kursor ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annisa Eka Haryati ◽  
Sugiyarto Sugiyarto ◽  
Rizki Desi Arindra Putri
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Clustering ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Subtractive Clustering ◽  
Validity Index ◽  
Fuzzy Clustering Method ◽  
Dependent Variables ◽  
Better Than

Multivariate statistics have related problems with large data dimensions. One method that can be used is principal component analysis (PCA). Principal component analysis (PCA) is a technique used to reduce data dimensions consisting of several dependent variables while maintaining variance in the data. PCA can be used to stabilize measurements in statistical analysis, one of which is cluster analysis. Fuzzy clustering is a method of grouping based on membership values ​​that includes fuzzy sets as a weighting basis for grouping. In this study, the fuzzy clustering method used is Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) with a combination of the Minkowski Chebysev distance. The purpose of this study was to compare the cluster results obtained from the FSC and FCM using the DBI validity index. The results obtained indicate that the results of clustering using FCM are better than the FSC.

Sensitivity analysis of factors affecting motion reliability of manipulator and fault diagnosis based on kernel principal component analysis

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Jing Yang ◽  
Lingyan Jin ◽  
Zejie Han ◽  
Deming Zhao ◽  
Ming Hu
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Component Analysis ◽  
Joint Clearance ◽  
Kernel Principal Component Analysis ◽  
Joint Rotation ◽  
Object A ◽  
Motion Performance ◽  
Flexibility Factor

Abstract As an important index to quantitatively measure the motion performance of a manipulator, motion reliability is affected by many factors, such as joint clearance. The present research utilized a UR10 manipulator as the research object. A factor mapping model for influencing the motion reliability was established. The link flexibility factor, joint flexibility factor, joint clearance factor, and Denavit–Hartenberg (DH) parameters were comprehensively considered in this model. The coupling relationship among the various factors was concisely expressed. Subsequently, the nonlinear response surface method was used to calculate the reliability and sensitivity of the manipulator, which provided an applicable reference for its trajectory planning and motion control. In addition, a data-driven fault diagnosis method based on the kernel principal component analysis (KPCA) was used to verify the motion accuracy and sensitivity of the manipulator, and joint rotation failure was considered as an example to verify the accuracy of the KPCA method. This study on the motion reliability of the manipulator is of great significance for the current motion performance, adjusting the control strategy and optimizing the completion effect of the motion task of a manipulator.

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