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Fault detection based on global-local PCA-SVDD for multimode processes
Li S(李帅); Zhou XF(周晓锋); Shi HB(史海波); Wang ZW(王中伟)
Department数字工厂研究室
Conference Name9th International Conference on Modelling, Identification and Control, ICMIC 2017
Conference DateJuly 10-12, 2017
Conference PlaceKunming, China
Source PublicationProceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages863-868
Indexed ByEI
EI Accession number20183105620632
Contribution Rank1
ISBN978-1-5090-6573-8
Keywordfault detection monitoring multimode industrial processes mode division global-local PCA-SVDD
AbstractFault detection is of importance for industrial processes with complex characteristics including multimode. In this paper, a fault detection method based on global-local PCA-SVDD is proposed for multimode industrial processes. Firstly, mode division based on spectral clustering is presented, which divides multimode processes into multiple modes without priori multimode information. Then, considering the multimode characteristic, global similarity and local non-similarity, the global-local PCA-SVDD models are built, which decomposes fault detection into a global model and multiple local models. Finally, different statistics and confidence limits are used for different models. The experiment results of the penicillin fermentation processes illustrate the feasibility and effectiveness of the proposed method for multimode industrial processes.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22361
Collection数字工厂研究室
Corresponding AuthorLi S(李帅)
AffiliationShenyang Institute of Automation, Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Li S,Zhou XF,Shi HB,et al. Fault detection based on global-local PCA-SVDD for multimode processes[C]. New York:IEEE,2017:863-868.
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