基于VMD和深度学习的EMU发动机轴承的异常振动信号检测
Yanjie Cui1, Weijiao Zhang2, Zhongkai Wang2
1Postgraduate Department, China Academy of Railway Sciences, Beijing 100081, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种新的方法,用于检测高速列车电机轴承振动中的异常,使用变化模式分解和深度学习. 该方法可靠地识别了复杂的操作数据中未知问题的早期迹象.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 高速电动多元单元 (EMU) 电机轴承面临着非静止振动信号和多元组件合的挑战.
- 现有的方法往往侧重于已知的故障诊断,而不是检测未知的异常状态.
- 来自地面监测系统的真实世界的振动数据对于检测早期异常至关重要.
研究的目的:
- 开发一个强大的EMU电机轴承异常检测系统.
- 为了解决振动信号中强烈的非静止性和多组件合的局限性.
- 为了识别超出已知的故障类型的未知操作状态的偏差.
主要方法:
- 适应变化模式分解 (VMD) 与功率光谱密度 (PSD) 进行优化参数选择.
- 这是一个混合深度学习模型,结合了卷积神经网络 (CNN),双向长期和短期记忆 (BiLSTM) 和残余网络 (ResNet).
- 一级支向量机 (OC-SVM) 训练了用于建立正常状态边界的预测错误.
主要成果:
- 优化的VMD参数克服了模式混合和带宽问题.
- 混合深度学习模型实现了精确的模态组件预测和信号重建.
- 在异常检测中,OC-SVM表现出高精度 (95.2%) 和F1得分 (0.909),显著超过了隔离森林 (F1得分=0.389).
结论:
- 协同的VMD和深度学习方法为EMU电机轴承的智能操作和维护提供了可靠的解决方案.
- 该方法有效地检测复杂,非静止的振动信号中的异常.
- 这种技术通过先进的条件监测提高了高速列车运营的安全性和效率.
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