Combination Method of Principal Component and Wavelet Analysis for Multivariate Process Monitoring and Fault Diagnosis

2003 ◽  
Vol 42 (18) ◽  
pp. 4198-4207 ◽  
Author(s):  
Ningyun Lu ◽  
Fuli Wang ◽  
Furong Gao
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Combination Method ◽  
Multivariate Process ◽  
Multivariate Process Monitoring

Multivariate process monitoring and fault diagnosis by multi-scale PCA

2002 ◽  
Vol 26 (9) ◽  
pp. 1281-1293 ◽  
Author(s):  
Manish Misra ◽  
H.Henry Yue ◽  
S.Joe Qin ◽  
Cheng Ling
Keyword(s):  
Fault Diagnosis ◽  
Process Monitoring ◽  
Multivariate Process ◽  
Multi Scale ◽  
Multivariate Process Monitoring

scholarly journals Process Monitoring and Fault Diagnosis for Piercing Production of Seamless Tube

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Dong Xiao ◽  
Jinhong Jiang ◽  
Yachun Mao ◽  
Xiaobo Liu
Keyword(s):  
Fault Diagnosis ◽  
Process Monitoring ◽  
Principal Component ◽  
Seamless Tube ◽  
Hollow Shell ◽  
Mechanism Model ◽  
Monitoring Model ◽  
Intelligent Model ◽  
Multiway Principal Component Analysis

With the development of modernization, the application of seamless tube becomes widespread. As the first process of seamless tube, piercing is vital for the quality of the tube. The solid round billet will be transformed into a hollow shell after the piercing process. The defects of hollow shell cannot be cleared in the following process, so a monitoring model for the quality of the hollow shell is important. But the piercing process is very complicated, and a mechanism model is difficult to build between the qualities of the hollow shell and measurement variables. Furthermore, an intelligent model is needed. We established two piercing process monitoring and fault diagnosis models based on the multiway principal component analysis (MPCA) model and the multistage MPCA model, respectively, and furthermore we made a comparison between these two concepts. We took three ways to divide the period based on process,K-means, and GA, respectively. Simulation experiments have shown that the multistate MPCA method has advantage over the MPCA method and the model based on the genetic algorithm (GA) can monitor the process effectively and detect the faults.

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