Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine
Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine
复制标题
DOI：
10.1016/j.mechmachtheory.2015.03.014
复制
发表时间：
2015-08-01
影响因子：
5.2
通讯作者：
Wang, Zili
中科院分区：
文献类型：
--
作者：
Tian, Ye;Ma, Jian;Wang, Zili
针对滚动轴承故障诊断中的热点和难点问题，提出了一种基于局部均值分解(LMD)-奇异值分解(SVD)和极值学习机(ELM)的智能故障诊断方法。采用一种新的自适应时频分析方法LMD将非线性、非平稳振动信号分解成一系列乘积函数，从乘积函数中得到具有物理意义的瞬时频率。然后，将奇异值分解应用于PFS，得到奇异值向量作为故障特征向量。最后，为了减少人为干预，缩短故障诊断时间，将ELM用于轴承故障的识别和分类。实验结果表明，该方法能够在较短的时间内准确诊断和识别滚动轴承在不同状态下的不同故障类型。(C)2015爱思唯尔有限公司。保留所有权利。
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.