A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis

TL;DR

This paper investigates the use of reinforcement learning, specifically Deep Q-Networks, for bearing fault diagnosis, showing potential for improved adaptability and comparable accuracy to traditional methods, with some computational challenges.

Contribution

It introduces reinforcement learning approaches for bearing fault diagnosis, highlighting their adaptability and potential to complement existing supervised learning methods.

Findings

RL models match traditional methods in controlled settings

RL excels in adaptability with optimized reward structures

Computational demands of RL models are significant

Abstract

Bearing faults in rotating machinery can lead to significant operational disruptions and maintenance costs. Modern methods for bearing fault diagnosis rely heavily on vibration analysis and machine learning techniques, which often require extensive labeled data and may not adapt well to dynamic environments. This study explores the feasibility of reinforcement learning (RL), specifically Deep Q-Networks (DQNs), for bearing fault classification tasks in machine condition monitoring to enhance the accuracy and adaptability of bearing fault diagnosis. The results demonstrate that while RL models developed in this study can match the performance of traditional supervised learning models under controlled conditions, they excel in adaptability when equipped with optimized reward structures. However, their computational demands highlight areas for further improvement. These findings demonstrate RL's potential to complement traditional methods, paving the way for adaptive diagnostic frameworks.