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首页> 外文期刊>Journal of Mechanical Science and Technology >A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine
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A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine

机译：一种新型滚动元件轴承故障分类方法结合了较低的力矩光谱和支持向量机

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摘要

Rolling-element bearings (REBs) faults are one of the most common breakdowns of rotating machines, thus proposing effective bearing fault diagnosis and classification methods is vital. In previous studies, lots of bearing fault classification methods have been proposed to solve the problem in low signal-to-noise ratio (SNR) conditions. Though satisfactory classification results have been obtained, in consideration of the practicability and application scenarios, there are still many aspects to improve, such as the complexity of method and the classification ability in lower SNR conditions. Therefore, this paper presents a novel method that combines lower-order moment spectrum with support vector machine (SVM) for bearing fault classification in low SNR conditions. The lower-order moment spectrum reduces influence of Gaussian noise and enhances the quality of fault feature. A bandpass filter group (BPFG) has been used to reduce the dimension of the lower-order moment spectra (LOMS) as feature vectors. And a following SVM has been applied as the fault classifier, due to the mature application and satisfactory performance in fault classification. The proposed method is demonstrated to have strong ability of classification in low SNR conditions experimentally.
机译：滚动元件轴承（REBS）故障是旋转机器最常见的故障之一，因此提出了有效的轴承故障诊断和分类方法至关重要。在先前的研究中，已经提出了许多轴承故障分类方法来解决低信噪比（SNR）条件下的问题。虽然已经获得了令人满意的分类结果，但考虑到实用性和应用方案，仍有许多方面来改善，例如方法的复杂性和较低的SNR条件下的分类能力。因此，本文介绍了一种新的方法，将低阶矩光谱与支持向量机（SVM）结合在低SNR条件下的轴承故障分类。较低的力矩谱减少了高斯噪声的影响并提高了故障特征的质量。已经使用带通滤波器组（BPFG）来减少低阶力矩光谱（LOMS）的尺寸作为特征向量。由于在故障分类中的成熟应用和令人满意的性能，因此已将以下SVM应用为故障分类器。所提出的方法经过实验的低SNR条件下具有强大分类能力。

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