scholarly journals Quality-Relevant Batch Process Fault Detection Using a Multiway Multi-Subspace CVA Method

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 23256-23265 ◽  
Author(s):  
Yuping Cao ◽  
Yongping Hu ◽  
Xiaogang Deng ◽  
Xuemin Tian
Keyword(s):  
Fault Detection ◽  
Batch Process

Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

2021 ◽  
pp. 014233122110447
Author(s):  
Hongjuan Yao ◽  
Xiaoqiang Zhao ◽  
Wei Li ◽  
Yongyong Hui
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Objective Function ◽  
Principal Component ◽  
Batch Process ◽  
Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Order Information ◽  
Multiple Neighborhoods

Batch process generally has varying dynamic characteristic that causes low fault detection rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This paper proposes a global enhanced multiple neighborhoods preserving embedding based fault detection strategy for dynamic batch process. Firstly, the angle neighbor is defined and selected to compensate for the insufficient expression for the spatial similarity of samples only by using the distance neighbor, and the time neighbor is introduced to describe the time correlations between samples. These three types of neighbors can fully characterize the similarity of the samples in time and space. Secondly, considering the minimum reconstruction error and the order information of three types of neighbors, an enhanced objective function is constructed to prevent the loss of order information when neighborhood preserving embedding (NPE) calculates the reconstruction weights. Furthermore, the enhanced objective function and a global objective function are organically combined to extract both global and local features, to describe process dynamics and visualize process data in a low-dimensional space. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The advantages of the proposed method over principal component analysis, neighborhood preserving embedding, dynamic principal component analysis and time NPE are demonstrated by a numerical example and the penicillin fermentation process simulation.

scholarly journals Fault Detection in a Batch Process Using a Bayesian Model.

1993 ◽  
Vol 26 (5) ◽  
pp. 465-469 ◽  
Author(s):  
Toshiyuki Nonaka ◽  
Yoshiyuki Yamashita ◽  
Mutsumi Suzuki
Keyword(s):  
Fault Detection ◽  
Bayesian Model ◽  
Batch Process
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