IAM: An Intuitive ANFIS-based method for stiction detection

Stiction in control valves is an industry-wide problem which results in degradation of control performance. A new approach to detect the presence of stiction by utilising only the PV-OP data from control loops is proposed using an Adaptive Neuro-fuzzy Inferencing System (ANFIS). Intuitively, the err...

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Veröffentlicht in:IOP conference series. Materials Science and Engineering 2018-12, Vol.458 (1), p.12054
Hauptverfasser: Jeremiah, Sean S, Zabiri, H, Ramasamy, M, Kamaruddin, B, Teh, W K, Mohd Amiruddin, A A A
Format: Artikel
Sprache:eng
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Zusammenfassung:Stiction in control valves is an industry-wide problem which results in degradation of control performance. A new approach to detect the presence of stiction by utilising only the PV-OP data from control loops is proposed using an Adaptive Neuro-fuzzy Inferencing System (ANFIS). Intuitively, the error between the output of an FIS model developed with stiction and a process with stiction would be minimal. When benchmarked against seventeen well-known industrial control loop case studies, the Intuitive ANFIS-based Method (IAM) accurately predicts the presence or absence of stiction in 65% of loops tested.
ISSN:1757-8981
1757-899X
1757-899X
DOI:10.1088/1757-899X/458/1/012054