期刊
IEEE ACCESS
卷 7, 期 -, 页码 12006-12015出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2892487
关键词
Multiple faults; hierarchical framework; real-time detection; accurate isolation; complex industrial processes
资金
- Natural Science Foundation of China (NSFC) [61873024, 61773053, 61673032]
- Fundamental Research Funds for the China Central Universities of USTB, China [FRF-BD-18-002A, FRF-GF-17-A4]
- National Key Research and Development Program of China [2017YFB0306403]
In actual production practice, the occurrence probability of multiple faults is much higher than that of a single fault. Since the composition of multiple faults is uncertain, it is difficult to establish a single model for multifault diagnosis. In this paper, a new hierarchical framework is proposed for solving multifault detection and isolation problems. First, an adaptive dynamic kernel independent component analysis method is proposed for time-varying and unknown multifault detection. After that, a sparse local exponential discriminant analysis method is developed for the multimodal multifault isolation problem. Finally, the Tennessee Eastman process is used to validate the performance of the proposed methods, and the experimental results show that the proposed methods can efficiently detect and isolate multiple faults.
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