Machine Learning-Based Framework for Real Time Detection and Early Prediction of Control Valve Stiction in Industrial Control Systems

TL;DR

This paper introduces a machine learning framework utilizing deep learning models to detect and predict control valve stiction in real time, enabling early intervention and maintenance in industrial systems.

Contribution

It presents the first ML-based method for early prediction of valve stiction using real industry data and routinely collected signals, with a focus on practical deployment.

Findings

LSTM achieved highest prediction accuracy.

Stiction can be predicted up to four hours in advance.

Framework can be integrated into existing control systems.

Abstract

Control valve stiction, a friction that prevents smooth valve movement, is a common fault in industrial process systems that causes instability, equipment wear, and higher maintenance costs. Many plants still operate with conventional valves that lack real time monitoring, making early predictions challenging. This study presents a machine learning (ML) framework for detecting and predicting stiction using only routinely collected process signals: the controller output (OP) from control systems and the process variable (PV), such as flow rate. Three deep learning models were developed and compared: a Convolutional Neural Network (CNN), a hybrid CNN with a Support Vector Machine (CNN-SVM), and a Long Short-Term Memory (LSTM) network. To train these models, a data-driven labeling method based on slope ratio analysis was applied to a real oil and gas refinery dataset. The LSTM model achieved the highest accuracy and was able to predict stiction up to four hours in advance. To the best of the authors' knowledge, this is the first study to demonstrate ML based early prediction of control valve stiction from real industry data. The proposed framework can be integrated into existing control systems to support predictive maintenance, reduce downtime, and avoid unnecessary hardware replacement.