基于VMD-RCMWPE特征提取和WOA-SVM优化多数据集融合的轴承故障诊断研究
Shouda Wang1, Chenglong Wang1, Youwei Lian1
1National Engineering Research Center for Technology and Equipment of Green Coating, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
采用一种新的方法来改善早期轴承故障诊断,该方法结合了变化模式分解 (VMD) 和精制的复合多尺度加权变量 (RCMWPE). 这种方法通过准确识别轴承故障来提高机械维护效率.
科学领域:
- 机械工程
- 信号处理
- 机器学习
背景情况:
- 工业机器的轴承故障会导致大量停机时间和成本.
- 由于复杂的振动信号特征 (非静止,非线性) 和噪音,难以准确的早期诊断.
- 现有的方法难以应对轴承振动数据的固有复杂性.
研究的目的:
- 为准确的轴承故障诊断开发综合方法.
- 应对非静止,非线性振动信号和噪声干扰所带来的挑战.
- 通过可靠的故障识别来改善机械维护决策.
主要方法:
- 变化模式分解 (VMD) 用于将信号分解为内在模式函数 (IMF).
- 用于从IMF中提取特征的精制复合多尺度加权转换 (RCMWPE).
- 用鱼优化算法 (WOA) 优化支持矢量机 (SVM) 来进行故障分类,并使用t-SNE来减少维度.
主要成果:
- 在PRONOSTIA数据集上的四类故障诊断达到96.5%的准确性.
- 在CWRU数据集上达到99.67%的准确性.
- 在不同数据集中表现出异常的故障识别能力,稳定性和通用性.
结论:
- 拟议的多数据集集成方法有效诊断轴承故障.
- VMD,RCMWPE和WOA优化的SVM的组合为复杂的故障模式提供了强大的解决方案.
- 这种方法为早期轴承故障预警和预测性维护提供了可行的技术途径.
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