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Updated: Jan 10, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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基于机器学习和SHAP可解释性分析的轴承故障诊断研究

Lulu Wang1, Menghua Wu2

  • 1Guangdong University of Science and Technology, Dongguan, 523668, China.

Scientific reports
|November 21, 2025
PubMed
概括

本研究引入了一种可解释的机器学习方法,用于诊断旋转机器中的轴承故障. XGBoost模型实现了高精度,证明了集成的多域特征和SHAP分析对预测性维护的有效性.

关键词:
轴承故障诊断 轴承故障诊断 轴承故障诊断 轴承故障诊断功能工程的特点工程.机器学习 机器学习预测性维护是一种预测性维护.SHAP的解释性可以解释.对振动信号进行分析.

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