Journal
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
Volume 24, Issue 3, Pages 928-939Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2015.2464331
Keywords
Analysis of relative changes; fault subspace selection; joint fault effects; multifault (MF) diagnosis; reconstruction modeling
Funding
- Program for the National Natural Science Foundation of China [61422306, 61433005, 61273166]
- Zhejiang Provincial Natural Science Foundation of China [LR13F030001]
- Zhejiang University K. P. Chao's High Technology Development Foundation
- Open Research Project through the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China [ICT1521]
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Online fault diagnosis has been a crucial task for industrial processes, which in general is taken after some abnormalities have been detected. Reconstruction-based fault diagnosis has been drawing special attention as a good alternative to the traditional contribution plot. It identifies the fault cause by finding the specific reconstruction model (i.e., fault subspace) that can well eliminate alarm signals from a bunch of alternatives that have been prepared based on historical fault data. However, in practice, the abnormality may result from the joint effects of multiple faults, which thus cannot be well corrected by single-fault subspace archived in the historical fault library. In this paper, an aggregative reconstruction-based fault diagnosis strategy is proposed to handle the case where multiple-fault causes jointly contribute to the abnormal process behaviors. First, fault subspaces are extracted based on historical fault data in two different monitoring subspaces where analysis of relative changes is taken to enclose the major fault effects that are responsible for different alarm monitoring statistics. Then, a fault subspace selection strategy is developed to analyze the combinatorial fault nature that will sort and select the informative fault subspaces by evaluating their significances in data correction. Finally, an aggregative fault subspace is calculated by combining the selected fault subspaces, which represents the joint effects from multiple faults and works as the final reconstruction model for online fault diagnosis. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with simulated multiple faults from the Tennessee Eastman benchmark process.
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