Abstract
As one of the most important elements of a control loop, control valves are essential assets to the plant because they ensure the high quality of products, as well as the safety of personnel and equipment (Abbasi et al. in J Hydrol, 597:125717, 2021). Unfortunately, control valves tend to suffer from many issues, and stiction is one of the long-standing faults that results in oscillations in important process variables which are highly undesirable. In the present work, unthresholded recurrence plots and texture analysis previously developed for mining industry (Kok et al. in IFAC-PapersOnLine 52:36-41, 2019) is applied to diagnose stiction in process control loops. Texture features are extracted from distance matrices derived from typical control-loop OP-PV data generated from a valve stiction model. A neural network model is then trained based on the extracted features. The optimised classification model is then applied in industrial control loops to identify the presence of stiction. The results from 78 benchmark industrial loops with varying faulty issues show a comparable performance with the recent methods reported in the literature.
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Acknowledgements
The authors would like to thank Universiti Teknologi PETRONAS (UTP), Malaysia and Curtin University, Australia for the technical support provided to complete this work. The authors also would like to thank The Ministry of Science, Technology, and Innovation of Malaysia which has funded this work via the eScienceFund (Project No.: 03-02-02-SF0236).
Funding
The authors also would like to thank The Ministry of Science, Technology, and Innovation of Malaysia which has funded this work via the eScienceFund (Project No.: 03-02-02-SF0236).
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Kok, T.L., Aldrich, C., Zabiri, H. et al. Application of unthresholded recurrence plots and texture analysis for industrial loops with faulty valves. Soft Comput 26, 10477–10492 (2022). https://doi.org/10.1007/s00500-022-06894-3
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DOI: https://doi.org/10.1007/s00500-022-06894-3