IoT and Machine Learning based Valve Stiction Estimation in Process Industry

Stiction in control valves represents an undesirable nonlinear phenomenon. Product quality and process safety frequently suffer due to the occurrence of valve stiction within process control systems utilizing control valves as their final control elements. This paper introduces a straightforward and...

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Bibliographic Details
Published in:	2024 Control Instrumentation System Conference (CISCON) pp. 1 - 6
Main Authors:	V, Vimal Kumar, K, Ragesh G., George, Aparna, S, Priya
Format:	Conference Proceeding
Language:	English
Published:	IEEE 02-08-2024
Subjects:	Control systems Internet of Things Machine learning MATLAB Maximum likelihood estimation Neural Network Neural networks Preventive maintenance Process control Safety Thing Speak Training Valve Stiction Valves
Online Access:	Get full text
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Summary:	Stiction in control valves represents an undesirable nonlinear phenomenon. Product quality and process safety frequently suffer due to the occurrence of valve stiction within process control systems utilizing control valves as their final control elements. This paper introduces a straightforward and pragmatic Machine Learning technique, supported by Internet of Things (IoT) technology, for identifying and assessing the initiation of stiction in all pertinent control loops on a periodic basis. Furthermore, this paper suggests an IoT-based innovative approach to facilitate collaboration among industrial experts across multiple process plants, enabling them to explore new avenues for preventive maintenance concerning stiction in control valves.
DOI:	10.1109/CISCON62171.2024.10696423