scholarly journals A Principal Component Analysis Control Chart Method for Catenary Status Evaluation and Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shan Lin ◽  
Liping Liu ◽  
Meiwan Rao ◽  
Shu Deng ◽  
Jiaxin Wang ◽  
...  

To make accurate and comprehensive evaluation of the catenary and diagnose the causes of the catenary fault, a method of catenary state evaluation and diagnosis based on the principal component analysis control chart was proposed, which can make full use of the multidimensional detection parameters of the catenary. The principal component analysis was used to reduce the dimension of catenary parameters, the principal component T2 control chart was calculated to show the change of principal component of catenary state data, the residual SPE control chart was calculated to show the change of their correlation, and the contribution rate control chart was calculated to show the cause of abnormal state data. The method can not only transform the multidimensional detection parameters of the catenary into a statistic to realize the simple and intuitive evaluation of the catenary state but also can accurately determine the cause of the abnormal state, so as to provide technical support for the targeted condition-based maintenance of the catenary.

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
RAHMAD RAHMAD WIDODO ◽  
I PUTU EKA NILA KENCANA ◽  
NI LUH PUTU SUCIPTAWATI

Controlling the quality of learning is very important and influences the accreditation of study programs at the Faculty of Mathematics and Natural Sciences Udayana University, as a guarantor of the quality of graduates. Apply pricipal component analysis to reduce the number of determinant attributes of learning quality, with the aim of looking at the data structure with fewer variables. The control chart is a multivariate control chart that is used to view the potrait of the quality of learning in the Mathematics and Natural Sciences Faculty, using new variables obtained from principal component analysis. The results obtained from principal component analysis show that the contribution of the learning quality indicators is univen. The potrait of the quality of learning at the Faculty of Mathematics and Natural Sciences obtained from the individual-moving range (I-MR) and the control chart shows the need for corrective actions and monitor regularly to improve the quality of learning.


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