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国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 特征与分类

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标 题： A Combination of WKNN to Fault Diagnosis of Rolling Element Bearings
WKNN与滚动轴承故障诊断的结合
作 者： Yaguo Lei
Zhengjia He
Yanyang Zi
关键词： Feature extraction  [特征提取]
Weighted K nearest neighbor combination  [ 加权K最近邻组合]
Rolling element bearing fault diagnosis  [ 滚动轴承故障诊断]
出版年： 2009
期刊名： Journal of Vibration and Acoustics
振动与声学杂志
年卷期： 2009, vol.131, no.6
摘 要： This paper presents a new method for fault diagnosis of rolling element bearings, which is developed based on a combination of weighted K nearest neighbor (WKNN) classifiers. This method uses wavelet packet transform based on the lifting scheme to pre-process the vibration signals before feature extraction. Time- and frequency-domain features are all extracted to represent the operation conditions of the bearings totally. Sensitive features are selected after feature extraction. And then, multiple classifiers based on WKNN are combined to overcome the two disadvantages of KNN and therefore it may enhance the classification accuracy. The experimental results of the proposed method to fault diagnosis of the rolling element bearings show that this method enables the detection of abnormalities in bearings and at the same time identification of fault categories and levels.

本文提出了一种基于加权K最近邻（WKNN）分类器组合的滚动轴承故障诊断新方法。该方法采用基于提升方案的小波包变换对振动信号进行预处理，再提取特征。通过提取时域和频域特征，全面反映轴承的工作状态。特征提取后，选取敏感性较高的特征。随后，将多个基于WKNN的分类器进行组合，以克服KNN方法存在的两个缺点，从而提高分类准确率。实验结果表明，该方法能够有效检测轴承异常，并同时实现故障类型与程度的识别。
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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