用强化学习提升轴承故障诊断的自适应能力，效果优于传统方法。
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis
- 采用DQN强化学习模型进行故障分类，通过优化奖励函数提升适应性。
- 在受控条件下性能媲美监督学习，在动态环境下表现更优。
- 适合需要实时调整的工业场景，为智能运维提供新思路。
旋转机械中的轴承故障可能导致重大运营中断和维修成本。当前的故障诊断方法主要依赖振动分析与机器学习技术，通常需要大量标注数据，且在动态环境中适应性差。本研究探讨了强化学习（特别是深度Q网络，DQN）在设备状态监测中用于轴承故障分类的可行性，旨在提升诊断的准确性和适应性。结果表明，尽管在受控条件下，所提出的强化学习模型性能可与传统监督学习模型相当，但通过优化奖励结构后，在动态环境中的适应性显著提升。然而，其较高的计算开销也揭示了进一步改进的空间。这些发现展示了强化学习在补充传统方法方面的潜力，为构建自适应诊断框架提供了可能。
原文摘要 · Abstract (English)
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.
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