Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 67, Issue 2, Pages 1316-1327Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2898576
Keywords
Accurate isolation; hierarchical detection; large-scale process; multiple faults; quality-related
Categories
Funding
- Natural Science Foundation of China [61873024, 61773053, 61673032]
- Fundamental Research Funds for the China Central Universities of the University of Science and Technology Beijing, China [FRF-BD-18-002A, FRF-GF-17-A4]
- National Key R&D Program of China [2017YFB0306403]
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This paper is devoted to the industrial practices and theoretical approaches for detection and isolation of quality-related multiple faults in large-scale processes. In contrast to the previous schemes, the main innovations are as follows: 1) it is the first time a hierarchical detection and isolation framework for quality-related multiple faults in large-scale processes is developed; 2) a combination method of adaptive kernel canonical variable analysis and Bayesian fusion for real-time and hierarchical detection of varying and unknown quality-related multiple faults is presented; and 3) a robust sparse exponential discriminant analysis algorithm for accurate isolation of multimode quality-related multiple faults is proposed. Finally, the whole framework is applied to a typical large-scale process, i.e., hot strip mill process, where the performance and effectiveness are further demonstrated from real industrial data.
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