A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings
A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings
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DOI：
10.1016/j.mechmachtheory.2014.01.011
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发表时间：
2014-05-01
影响因子：
5.2
通讯作者：
Han, Minghong
中科院分区：
文献类型：
--
作者：
Liu, Huanhuan;Han, Minghong
提出一种基于局部均值分解技术和多尺度熵的故障特征提取方法。当滚子轴承发生故障时，所拾取的振动信号准确地表现出非平稳特性。仅通过传统的时域方法或频域方法很难对滚子轴承的工作状况做出准确的评估。因此，采用局部均值分解法这种新型自适应时频方法作为预处理，将滚子轴承的非平稳振动信号分解为多个乘积函数。此外，这里还介绍了多尺度熵，是指跨一系列尺度的样本熵的计算。每个乘积函数的多尺度熵可以计算为特征向量。对实际轴承振动信号的分析结果表明，该方法是有效的。 (C) 2014 Elsevier Ltd. 保留所有权利。
A novel fault feature extraction method based on the local mean decomposition technology and multi-scale entropy is proposed in this paper. When fault occurs in roller bearings, the vibration signals picked up would exactly display non-stationary characteristics. It is not easy to make an accurate evaluation on the working condition of the roller bearings only through traditional time-domain methods or frequency-domain methods. Therefore, local mean decomposition method, a new self-adaptive time-frequency method, is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of product functions. Furthermore, the multi-scale entropy, referring to the calculation of sample entropy across a sequence of scales, is introduced here. The multi-scale entropy of each product function can be calculated as the feature vectors. The analysis results from practical bearing vibration signals demonstrate that the proposed method is effective. (C) 2014 Elsevier Ltd. All rights reserved.