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首页> 外文期刊>Journal of Vibration and Acoustics >A Combination of WKNN to Fault Diagnosis of Rolling Element Bearings
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A Combination of WKNN to Fault Diagnosis of Rolling Element Bearings

机译：WKNN结合在滚动轴承故障诊断中的应用

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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.
机译：本文提出了一种基于加权K最近邻（WKNN）分类器的滚动轴承故障诊断方法。该方法利用基于提升方案的小波包变换对特征提取之前的振动信号进行预处理。提取时域和频域特征以完全代表轴承的运行状况。特征提取后选择敏感特征。然后，结合基于WKNN的多个分类器，克服了KNN的两个缺点，可以提高分类的准确性。所提出的滚动轴承故障诊断方法的实验结果表明，该方法能够检测出轴承中的异常情况，并能同时识别出故障的类别和等级。

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