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Monitoring and fault diagnosis for industrial process
Li S(李帅); Zhou XF(周晓锋); Shi HB(史海波); Zheng ZY(郑泽宇)
Department数字工厂研究室
Conference Name2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
Conference DateJune 8-12, 2015
Conference PlaceShenyang, China
Source Publication2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages1356-1361
Indexed ByEI ; CPCI(ISTP)
EI Accession number20161402187679
WOS IDWOS:000380502300248
Contribution Rank1
ISSN2379-7711
ISBN978-1-4799-8730-6
KeywordManifold Learning Contribution Analysis Monitoring Fault Diagnosis
AbstractMonitoring and fault diagnosis are crucial for industrial process. In this paper, a simple and efficient manifold learning method is used for process monitoring and fault diagnosis. Firstly, local neighbor relationship of process data is used for process modelling, which divides process data into the embedding space and residual space. Then, different statistics and confidence limits are computed, which can be used for monitoring. Finally, the contribution analysis based on manifold learning is used for fault diagnosis. When the fault variables are found, quality control can be introduced to improve production safety and quality stabilization in industrial process. The manifold learning method is applied for one practical foods industrial production process. The experiment results show the feasibility and efficiency of the manifold learning method for monitoring and fault diagnosis.
Language英语
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/17353
Collection数字工厂研究室
AffiliationShenyang Institute of Automation, Chinese Academy of Sciences, 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. Monitoring and fault diagnosis for industrial process[C]. Piscataway, NJ, USA:IEEE,2015:1356-1361.
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