Entropy principal component analysis and its application to nonlinear chemical process fault diagnosis

2014 ◽  
Vol 9 (5) ◽  
pp. 696-706 ◽  
Author(s):  
Xiaogang Deng ◽  
Xuemin Tian
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Chemical Process ◽  
Principal Component ◽  
Component Analysis ◽  
Process Fault Diagnosis

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

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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