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A sample entropy inspired affinity propagation method for bearing fault signal classification
Digital Signal Processing ( IF 2.9 ) Pub Date : 2020-04-09 , DOI: 10.1016/j.dsp.2020.102740
Chaojie Wang

For bearing fault signal classification, the performance of the affinity propagation (AP) method is limited by its way of measuring similarity. In this paper, we have proposed an improved version for the AP method, which is termed as IMFSE-AP. The key point of the proposed IMFSE-AP method is the improvement on the way of measuring signal similarity. Instead of the Euclidean distance, a novel method based on the sample entropy of IMFs (IMFSE) is proposed, which can measure signal similarities better with respect to data complexity. In the proposed similarity measurement method, signals are first decomposed into IMFs by the EEMD method. Then the distances of sample entropy vectors of IMFs are computed to evaluate similarities between signals. Numerical experiments conducted on synthetic signals and real bearing fault signals illustrate the good performance of the proposed method.



中文翻译:

样本熵启发的亲和力传播方法用于轴承故障信号分类

对于轴承故障信号分类,亲和度传播(AP)方法的性能受到其测量相似性的方式的限制。在本文中,我们为AP方法提出了一种改进版本,称为IMFSE-AP。提出的IMFSE-AP方法的重点是对信号相似度的测量方法的改进。代替欧几里德距离,提出了一种基于IMF样本熵(IMFSE)的新方法,该方法可以更好地测量数据相似度方面的信号相似性。在提出的相似性测量方法中,首先通过EEMD方法将信号分解为IMF。然后,计算IMF的样本熵矢量的距离,以评估信号之间的相似性。

更新日期:2020-04-20
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