Model Migration Based on Subspace Separation for Development of a New Process Monitoring Model

2014 ◽  
Author(s):  
Haozhi Liu ◽  
Bugong Xu ◽  
Furong Gao
Keyword(s):  
Process Monitoring ◽  
Monitoring Model ◽  
New Process ◽  
Model Migration ◽  
Subspace Separation

Time domain NMR as a new process monitoring method for characterization of pharmaceutical hydrates

2017 ◽  
Vol 137 ◽  
pp. 96-103 ◽  
Author(s):  
Stefanie Ulrike Schumacher ◽  
Benno Rothenhäusler ◽  
Alf Willmann ◽  
Jürgen Thun ◽  
Regina Moog ◽  
...  
Keyword(s):  
Process Monitoring ◽  
Time Domain ◽  
Monitoring Method ◽  
New Process

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.

NXE:3400 OPC process monitoring: model validity vs process variability

2021 ◽  
Author(s):  
Dongbo Xu ◽  
David Rio ◽  
Werner Gillijns ◽  
Max Delorme ◽  
Christina Baerts
Keyword(s):  
Process Monitoring ◽  
Process Variability ◽  
Monitoring Model ◽  
Model Validity

scholarly journals Calibration, Prediction and Process Monitoring Model Based on Factor Analysis for Incomplete Process Data

2005 ◽  
Vol 38 (12) ◽  
pp. 1025-1034 ◽  
Author(s):  
Dong Soon Kim ◽  
Chang Kyoo Yoo ◽  
Young Il Kim ◽  
Jae Hak Jung ◽  
In-Beum Lee
Keyword(s):  
Factor Analysis ◽  
Process Monitoring ◽  
Process Data ◽  
Model Based ◽  
Monitoring Model

scholarly journals A New Process Monitoring Method Based on Waveform Signal by Using Recurrence Plot

Entropy ◽  
2015 ◽  
Vol 17 (12) ◽  
pp. 6379-6396 ◽  
Author(s):  
Cheng Zhou ◽  
Weidong Zhang
Keyword(s):  
Process Monitoring ◽  
Recurrence Plot ◽  
Monitoring Method ◽  
New Process
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