Paper Info
Title
Quality-Relevant Batch Process Fault Detection Using a Multiway Multi-Subspace CVA Method.
Abstract
For batch process fault detection, regular data-driven methods cannot distinguish quality-irrelevant faults from quality-relevant faults. To solve such problem, we propose a multiway multi-subspace canonical variate analysis (MMCVA) method for the batch processes. First, the combination of batch-wise unfolding and variable-wise unfolding is adopted to unfold the three-way process and quality data in to two-way data. Then, we use CVA to project the process and quality data spaces to three subspaces, a process-quality correlated subspace, a quality-uncorrelated process subspace, and a process-uncorrelated quality subspace. Fault detection statistics are developed based on the three subspaces. The proposed MMCVA method is capable of indicating the normality or abnormality of the quality variables, while detecting a process fault. The simulation results of a fed-batch penicillin fermentation process illustrate the effectiveness of the proposed method.
Year
DOI
Venue
2017
10.1109/ACCESS.2017.2764538
IEEE ACCESS
Keywords
Field
DocType
Fault detection,batch process,quality monitoring,canonical variate analysis,principal component analysis
Normality,Data mining,Pattern recognition,Subspace topology,Computer science,Fault detection and isolation,Linear subspace,Batch processing,Artificial intelligence,Canonical variate analysis,Distributed computing
Journal
Volume
ISSN
Citations 
5
2169-3536
0
PageRank 
References 
Authors
0.34
7
4
Name
Order
Citations
PageRank
Yuping Cao102.03
Yongping Hu200.34
Deng Xiaogang311517.49
Xuemin Tian4717.54