A Feature Extraction Method Using Improved Multi-Scale Entropy for Rolling Bearing Fault Diagnosis

作者：Ju, Bin; Zhang, Haijiao; Liu, Yongbin^*; Liu, Fang; Lu, Siliang; Dai, Zhijia

来源：Entropy, 2018, 20(4): 212.

DOI：10.3390/e20040212

• improved multi-scale entropy
• multi-scale entropy
• feature extraction
• bearing fault diagnosis

A feature extraction method named improved multi-scale entropy (IMSE) is proposed for rolling bearing fault diagnosis. This method could overcome information leakage in calculating the similarity of machinery systems, which is based on Pythagorean Theorem and similarity criterion. Features extracted from bearings under different conditions using IMSE are identified by the support vector machine (SVM) classifier. Experimental results show that the proposed method can extract the status information of the bearing. Compared with the multi-scale entropy (MSE) and sample entropy (SE) methods, the identification accuracy of the features extracted by IMSE is improved as well.

• 出版日期2018-4
• 单位安徽大学