DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System

TL;DR

This paper introduces DQLAP, a deep reinforcement learning-based algorithm for fault detection in industrial systems, improving accuracy and prediction speed through an adaptive update policy.

Contribution

The paper presents a novel deep Q-learning framework with an update policy tailored for fault diagnosis in steam turbine systems, enhancing detection accuracy and efficiency.

Findings

3% increase in evaluation metrics

Improved prediction speed

4% improvement over traditional neural networks

Abstract

In modern industrial systems, diagnosing faults in time and using the best methods becomes more and more crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning have proposed various methods for data-based fault diagnosis, and we are looking for the most reliable and practical ones. This paper aims to develop a framework based on deep learning and reinforcement learning for fault detection. We can increase accuracy, overcome data imbalance, and better predict future defects by updating the reinforcement learning policy when new data is received. By implementing this method, we will see an increase of $3\%$ in all evaluation metrics, an improvement in prediction speed, and $3\%$ - $4\%$ in all evaluation metrics compared to typical backpropagation multi-layer neural network prediction with similar parameters.