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

2025-06-24

AI Summary

To address the scarcity of labeled data and the limited adaptability of conventional supervised learning in bearing fault diagnosis under dynamic operating conditions, this paper pioneers the application of Deep Q-Networks (DQN) to bearing fault classification. We propose a joint framework integrating vibration-signal feature extraction with DQN and design an operating-condition-aware sparse reward mechanism, significantly enhancing model generalization under non-stationary scenarios—such as varying rotational speeds and loads. Experimental results demonstrate that, given limited labeled data, our method achieves diagnostic accuracy comparable to CNN-based supervised models while exhibiting superior transferability to unseen operating conditions. This work validates the feasibility and distinctive advantages of reinforcement learning paradigms in intelligent fault diagnosis, offering a novel solution for small-sample, highly dynamic industrial settings.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Learning with Manifolds
• Intelligent Robots: Learning & Optimization for ROB

Application Category

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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.

Problem

• Comparing RL and deep learning for bearing fault diagnosis
• Enhancing accuracy and adaptability in fault classification
• Reducing reliance on extensive labeled training data

Innovation

• Uses Deep Q-Networks for fault diagnosis
• Optimizes reward structures for adaptability
• Compares RL with traditional supervised learning