对变速箱故障严重程度分类的声学辐射的多尺度分析
René-Vinicio Sánchez1, Yu Liu2,3, Huafeng Qin4,5
1Universidad Politécnica Salesiana, GIDTEC, Cuenca, 010105, Ecuador. rsanchezl@ups.edu.ec.
Scientific reports
|February 4, 2026
概括
本研究介绍了复合层次多尺度 (CHMSE) 用于分析变速箱的声辐射信号. CHMSE与随机森林相结合,在诊断变速箱故障方面取得了超过97%的准确性,提高了预测性维护.
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:
- 声辐射 (AE) 传感器对于通过监测高频过渡变量来早期检测旋转机械的故障至关重要.
- 由于各种损坏机制,从复杂的AE信号中自动分类变速箱故障严重程度具有挑战性.
研究的目的:
- 调查多尺度方法,以计算高效地从AE信号中提取特征,用于变速箱故障诊断.
- 为了比较复合多尺度 (CMSE),层次多尺度 (HMSE) 和复合层次多尺度 (CHMSE) 的故障严重性分类.
主要方法:
- 从9个严重程度的杆变速箱测试装置收集的实验AE数据,其中存在诱导故障 (插孔,破牙,根裂,痕).
- 使用CMSE,HMSE和CHMSE提取了16个非线性信息特征.
- 使用随机森林 (RF) 和其他机器学习模型进行分类,并使用SHAP进行解释.
主要成果:
- 与射频模型相结合的CHMSE方法实现了最高的分类准确性 (97.37-99.50%),优于单级方法和统计特征.
- 层次方法 (HMSE,CHMSE) 显示出显著的性能改善 (p < 0.05).
- 雷尼,萨利斯,值和日志能量被确定为关键的区分因素.
结论:
- 基于CHMSE的特征提取提供了一个计算效率高且高度准确的方法,用于从AE信号中诊断变速箱故障严重程度.
- 这种方法显著提高了基于AE的条件监测系统的可靠性,用于工业变速箱的预测性维护.
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