基于深度强化学习的结合神经元模型的优化以及该模型在轴承故障诊断中的应用
Shan Wang1,2, Jiaxiang Li1,2, Xinsheng Xu1,2
1Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了一种用于轴承故障诊断的新型合神经元模型,通过深度强化学习算法增强. 该方法显著提高了机械设备的信号噪声比和故障识别精度.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 轴承是机械系统中至关重要的组件,其故障可能会导致显著的性能下降.
- 随机共振 (SR) 方法有效地增强轴承振动信号中的故障特征能量,使其成为轴承故障诊断的关键研究领域.
研究的目的:
- 提出一种新的结合神经元模型,其灵感来源于生物随机共振,用于处理轴承振动信号.
- 开发一个改进的深度强化学习 (DRL) 算法,具有优先级的经验重复缓冲区,用于优化合神经元模型参数.
- 评估拟议的DRL优化模型在提高轴承故障诊断方面的有效性.
主要方法:
- 一个基于生物随机共振的结合神经元模型被开发出来.
- 为参数优化设计了一种改进的DRL算法,其中包含了优先级的体验重播缓冲区.
- DRL算法对重播缓冲元参数进行了数据选,以预测合神经元模型的性能,使用信号噪声比 (SNR) 作为评估指标.
- 模拟和实验室收集的变速箱轴承故障振动信号被处理.
主要成果:
- 经过DLR优化的合神经元模型实现了 -13.0407dB的最佳信号噪声比.
- 经过DRL优化的模型显示,轴承故障的识别率为100%.
- 对比分析显示,DLR优化模型的表现优于传统的强化学习,粒子群优化和量子粒子群优化算法.
结论:
- 拟议的DRL优化合神经元模型为轴承故障诊断提供了显著的优势.
- 该方法有效地提高了轴承故障固有频率的能量,提供了更有效和更准确的解决方案.
- 这种方法对于预测性维护和机械健康监测具有重要的工程应用价值.
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