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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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Elsevier Ltd
2019
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| Online Access: | 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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| Summary: | 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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