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首页> 外文期刊>Mechanism and Machine Theory: Dynamics of Machine Systems Gears and Power Trandmissions Robots and Manipulator Systems Computer-Aided Design Methods >Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine
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Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine

机译：使用LMD-SVD和极限学习机在可变条件下进行滚动轴承故障诊断

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Fault diagnosis for rolling bearings under variable conditions is a hot and relatively difficult topic, thus an intelligent fault diagnosis method based on local mean decomposition (LMD)-singular value decomposition (SVD) and extreme learning machine (ELM) is proposed in this paper. LMD, a newself-adaptive time-frequency analysis method, was applied to decompose the nonlinear and non-stationary vibration signals into a series of product functions (PFs), from which instantaneous frequencies with physical significance can be obtained. Then, the singular value vectors, as the fault feature vectors, were acquired by applying SVD to the PFs. Last, for the purpose of lessening human intervention and shortening the fault-diagnosis time, ELM was introduced for identification and classification of bearing faults. From the experimental results it was concluded that the proposed method can accurately diagnose and identify different fault types of rolling bearings under variable conditions in a relatively shorter time. (C) 2015 Elsevier Ltd. All rights reserved.
机译：滚动轴承在变工况下的故障诊断是一个热门且相对较难的课题，因此提出了一种基于局部均值分解（LMD）-奇异值分解（SVD）和极限学习机（ELM）的智能故障诊断方法。应用LMD，一种新的自适应时频分析方法，将非线性和非平稳振动信号分解为一系列乘积函数（PF），从中可以获得具有物理意义的瞬时频率。然后，通过将SVD应用于PF来获取作为故障特征向量的奇异值向量。最后，为了减少人为干预并缩短故障诊断时间，引入了ELM来对轴承故障进行识别和分类。从实验结果可以得出结论，该方法可以在相对较短的时间内，准确地诊断和识别各种情况下滚动轴承的不同故障类型。 （C）2015 Elsevier Ltd.保留所有权利。

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