对于滚动元件轴承的故障严重程度分类的高级深度学习方法
Vivek Parmar1, Shirshendu Layek2, Megha Bhushan3
1Thapar Institute of Engineering & Technology, 147004, Patiala, India.
Scientific reports
|October 2, 2025
概括
本研究介绍了一种使用参数优化的变化模式分解 (VMD) 和深度学习 (DL) 进行轴承故障分析的混合方法. 该方法准确地识别了轴承缺陷及其严重程度,优于其他方法.
科学领域:
- 机械工程 机械工程
- 机器学习 机器学习
- 状态监控 状态监控
背景情况:
- 轴承故障对工业机械的性能和安全有重大影响.
- 准确的故障诊断和严重程度评估对于预测性维护至关重要.
- 传统的方法经常与局部缺陷的复杂振动信号作斗争.
研究的目的:
- 开发一种混合方法来分析轴承故障及其严重程度.
- 为了利用参数优化的变化模式分解 (VMD) 和深度学习 (DL) 算法.
- 提高轴承状况监测的准确性和可靠性.
主要方法:
- 一种混合方法,将参数优化的VMD用于信号预处理和DL用于分类.
- 在自我调整轴承中的局部缺陷的人工播种,以生成振动数据 (案例研究II).
- 使用粒子群优化优化VMD参数的优化.
- 实施和验证七个DL算法,包括1D-CNN,WaveNet和Gated Recurrent Unit.
- 根据标准数据集进行验证 (西储大学案例 - 案例研究I).
主要成果:
- 参数优化的VMD有效地从振动信号中提取相关的内在模式功能.
- 在故障严重程度分类方面,1D-CNN,WaveNet和Gated Recurrent Unit实现了高精度 (分别为99.65%,97.05%,97.33%).
- 混合方法论在标准和生成数据集上都显示出高可靠性.
结论:
- 拟议的混合VMD-DL方法为轴承故障诊断和严重性分析提供了强大而准确的解决方案.
- 像1D-CNN,WaveNet和GRU这样的先进DL模型显示出对现实世界状态监控应用的重大承诺.
- 这种方法有助于改进机械健康管理和减少运营停机时间.
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