Valve stiction detection through improved pattern recognition using neural networks

• Main Authors: Mohd Amiruddin, A.A.A., Zabiri, H., Jeremiah, S.S., Teh, W.K., Kamaruddin, B.
• Format: Article
• Published: Elsevier Ltd 2019
• 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/

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