Valve stiction detection through improved pattern recognition using neural networks
A non-invasive method for detecting valves suffering from stiction using multi-layer feed-forward neural networks (NN) is proposed, via a simple class-based diagnosis. The proposed Stiction Detection Network (SDN) uses a transformation of PV (process variable) and OP (controller output) operational...
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my.utp.eprints.249802021-08-27T08:35:11Z Valve stiction detection through improved pattern recognition using neural networks Mohd Amiruddin, A.A.A. Zabiri, H. Jeremiah, S.S. Teh, W.K. Kamaruddin, B. A non-invasive method for detecting valves suffering from stiction using multi-layer feed-forward neural networks (NN) is proposed, via a simple class-based diagnosis. The proposed Stiction Detection Network (SDN) uses a transformation of PV (process variable) and OP (controller output) operational data. Verification of the proposed SDN model's detection accuracy is done through cross-validation with generated samples and benchmarking with various industrial loops. The industrial loop benchmark predictions of the proposed SDN method has a combined accuracy of 78 (75 in predicting stiction, and 81 for non-stiction) in predicting loop condition, matching capabilities of other established methods in accurately predicting realistic industrial loops suffering from stiction, while also being applicable to all types of oscillatory control signals. © 2019 Elsevier Ltd 2019 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067911670&doi=10.1016%2fj.conengprac.2019.06.008&partnerID=40&md5=452b38cf250195cc2f4f992801634202 Mohd Amiruddin, A.A.A. and Zabiri, H. and Jeremiah, S.S. and Teh, W.K. and Kamaruddin, B. (2019) Valve stiction detection through improved pattern recognition using neural networks. Control Engineering Practice, 90 . pp. 63-84. http://eprints.utp.edu.my/24980/ |
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A non-invasive method for detecting valves suffering from stiction using multi-layer feed-forward neural networks (NN) is proposed, via a simple class-based diagnosis. The proposed Stiction Detection Network (SDN) uses a transformation of PV (process variable) and OP (controller output) operational data. Verification of the proposed SDN model's detection accuracy is done through cross-validation with generated samples and benchmarking with various industrial loops. The industrial loop benchmark predictions of the proposed SDN method has a combined accuracy of 78 (75 in predicting stiction, and 81 for non-stiction) in predicting loop condition, matching capabilities of other established methods in accurately predicting realistic industrial loops suffering from stiction, while also being applicable to all types of oscillatory control signals. © 2019 |
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Article |
author |
Mohd Amiruddin, A.A.A. Zabiri, H. Jeremiah, S.S. Teh, W.K. Kamaruddin, B. |
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Mohd Amiruddin, A.A.A. Zabiri, H. Jeremiah, S.S. Teh, W.K. Kamaruddin, B. Valve stiction detection through improved pattern recognition using neural networks |
author_facet |
Mohd Amiruddin, A.A.A. Zabiri, H. Jeremiah, S.S. Teh, W.K. Kamaruddin, B. |
author_sort |
Mohd Amiruddin, A.A.A. |
title |
Valve stiction detection through improved pattern recognition using neural networks |
title_short |
Valve stiction detection through improved pattern recognition using neural networks |
title_full |
Valve stiction detection through improved pattern recognition using neural networks |
title_fullStr |
Valve stiction detection through improved pattern recognition using neural networks |
title_full_unstemmed |
Valve stiction detection through improved pattern recognition using neural networks |
title_sort |
valve stiction detection through improved pattern recognition using neural networks |
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Elsevier Ltd |
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2019 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067911670&doi=10.1016%2fj.conengprac.2019.06.008&partnerID=40&md5=452b38cf250195cc2f4f992801634202 http://eprints.utp.edu.my/24980/ |
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