🤖 AI Summary
This study addresses valve stiction—a critical cause of industrial process instability, equipment wear, and increased maintenance costs—by proposing a machine learning framework that enables real-time detection and up to four-hour-ahead prediction of stiction faults using only standard controller output (OP) and process variable (PV) signals, without requiring additional sensors. A novel data-driven labeling approach based on slope ratio analysis is introduced to train convolutional neural network (CNN), CNN–support vector machine (SVM), and long short-term memory (LSTM) models. Among these, the LSTM model demonstrates superior performance, achieving high prediction accuracy on real-world industrial data. This work represents the first successful implementation of early stiction prediction solely from routine process data, offering a practical foundation for predictive maintenance and significantly reducing unnecessary hardware replacements.
📝 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.