An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process
Focusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering inf...
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Online Access: | http://dx.doi.org/10.1155/2017/9560206 |
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doaj-0ec4f8e11b124c1e8a56ea7ea8027a772020-11-24T21:26:06ZengHindawi LimitedJournal of Control Science and Engineering1687-52491687-52572017-01-01201710.1155/2017/95602069560206An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial ProcessKaixiang Peng0Bingzheng Wang1Jie Dong2Key Laboratory for Advanced Control of Iron and Steel Process, Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory for Advanced Control of Iron and Steel Process, Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory for Advanced Control of Iron and Steel Process, Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaFocusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering information of original training data can be acquired. Secondly, according to multimode characteristics of process data, the monitoring model integrated Gaussian mixture model with total projection to latent structures is effective after building the covariance description form. The multimode total projection to latent structures (MTPLS) model is the foundation of problem solving about quality-related monitoring for multimode processes. Then, a comprehensive statistics index is defined which is based on the posterior probability of the monitored samples belonging to each Gaussian component in the Bayesian theory. After that, a combined index is constructed for process monitoring. Finally, motivated by the application of traditional contribution plot in fault diagnosis, a gradient contribution rate is applied for analyzing the variation of variable contribution rate along samples. Our method can ensure the implementation of online fault monitoring and diagnosis for multimode processes. Performances of the whole proposed scheme are verified in a real industrial, hot strip mill process (HSMP) compared with some existing methods.http://dx.doi.org/10.1155/2017/9560206 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kaixiang Peng Bingzheng Wang Jie Dong |
spellingShingle |
Kaixiang Peng Bingzheng Wang Jie Dong An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process Journal of Control Science and Engineering |
author_facet |
Kaixiang Peng Bingzheng Wang Jie Dong |
author_sort |
Kaixiang Peng |
title |
An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process |
title_short |
An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process |
title_full |
An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process |
title_fullStr |
An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process |
title_full_unstemmed |
An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process |
title_sort |
efficient quality-related fault diagnosis method for real-time multimode industrial process |
publisher |
Hindawi Limited |
series |
Journal of Control Science and Engineering |
issn |
1687-5249 1687-5257 |
publishDate |
2017-01-01 |
description |
Focusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering information of original training data can be acquired. Secondly, according to multimode characteristics of process data, the monitoring model integrated Gaussian mixture model with total projection to latent structures is effective after building the covariance description form. The multimode total projection to latent structures (MTPLS) model is the foundation of problem solving about quality-related monitoring for multimode processes. Then, a comprehensive statistics index is defined which is based on the posterior probability of the monitored samples belonging to each Gaussian component in the Bayesian theory. After that, a combined index is constructed for process monitoring. Finally, motivated by the application of traditional contribution plot in fault diagnosis, a gradient contribution rate is applied for analyzing the variation of variable contribution rate along samples. Our method can ensure the implementation of online fault monitoring and diagnosis for multimode processes. Performances of the whole proposed scheme are verified in a real industrial, hot strip mill process (HSMP) compared with some existing methods. |
url |
http://dx.doi.org/10.1155/2017/9560206 |
work_keys_str_mv |
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1725981010042028032 |