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国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 信号处理

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标 题： A multi fault classification in a rotor-bearing system using machine learning approach
基于机器学习方法的转子轴承系统多故障分类
作 者： Shinde, Prasad V.
Desavale, R. G.
Jadhav, P. M.
Sawant, Sanjay H.
关键词： Fault diagnosis  [故障诊断]
KNN  [ KNN]
Bearing characteristics feature  [ 轴承特性特征]
Signal processing  [ 信号处理]
SUPPORT VECTOR MACHINE  [ 支持向量机]
DIAGNOSIS  [ 诊断]
出版年： 2023
期刊名： Journal of the Brazilian Society of Mechanical Sciences and Engineering
巴西机械科学与工程学会期刊
年卷期： 2023, vol.45, no.2
摘 要： Modern condition monitoring of rotating machinery became intelligent for enhanced reliability, productivity, and safety. Signal processing has been collaboratively implemented with several machine learning approaches to increase the effectiveness of the fault diagnosis. This paper explores fundamental bearing frequencies withdrawn from a vibration response as novel extracted features. Experimentally obtained vibration data at diverse operating conditions have been analyzed and supplied to a supervised machine learning algorithm K nearest neighbor network (KNN) for fault classification. The result shows that the KNN algorithm based on the novel features provides 98.5% fault classification accuracy and feels promising for condition monitoring of industrial rotating machines.

旋转机械的现代状态监测已实现智能化，以提高可靠性、生产效率和安全性。信号处理与多种机器学习方法相结合，以增强故障诊断的有效性。本文探讨了从振动响应中提取出的基本轴承频率作为新颖的特征。通过实验获取了不同运行工况下的振动数据，并将其输入到监督式机器学习算法K最近邻网络（KNN）进行故障分类。结果表明，基于新特征的KNN算法实现了98.5%的故障分类准确率，对于工业旋转机械的状态监测具有良好的应用前景。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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