可解释的故障诊断变速机械通过自适应引和因果学习
Xuanshi Chen1, Manyi Wang1, Meijun Wang1
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
ISA transactions
|November 28, 2025
概括
本研究引入了一种用于诊断不同速度运行的旋转机械故障的新方法. 该框架可以准确地识别机器故障而不需要计时器,从而提高诊断性能.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 工业过程中的旋转机械通常在变速条件下运行.
- 传统的诊断技术在这些条件下难以准确地提取故障特征.
- 准确的故障诊断对于防止设备故障和确保运营效率至关重要.
研究的目的:
- 开发一种新的诊断框架,用于对变速旋转机器的故障诊断.
- 解决时间变速环境中常规方法的局限性.
- 为工业应用提供可解释且无计时器的解决方案.
主要方法:
- 集成高分辨率时间频率分析 (同步挤压连续波段变换 - SSQ-CWT) 和可解释的因果学习.
- 使用自适应旋转频率提取 (ARFRE) 算法进行无计时器即时速度估计.
- 使用因果森林进行特征选和构建可解释的决策树用于故障诊断.
主要成果:
- 该框架实现了高诊断准确率:98.49% (SQV),100.00% (太华) 和97.44% (工业数据集).
- 与基线方法相比,在故障诊断中表现出优异的性能.
- 成功地确定了故障状态的因果相关特征,从而形成了一个可解释的决策树.
结论:
- 拟议的框架为变速机械的故障诊断提供了一个强大,可解释和无计时器的解决方案.
- SSQ-CWT,ARFRE和因果森林的整合提供了增强的诊断能力.
- 该方法显示了在工业环境中用于预测性维护的实际应用的巨大潜力.
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