A Feature Extraction Method Using Improved Multi-Scale Entropy for Rolling Bearing Fault Diagnosis
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College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
2
National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei 230601, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(4), 212; https://doi.org/10.3390/e20040212
Received: 6 February 2018
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Revised: 16 March 2018
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Accepted: 19 March 2018
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Published: 21 March 2018
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.
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Keywords:
improved multi-scale entropy; multi-scale entropy; feature extraction; bearing fault diagnosis
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MDPI and ACS Style
Ju, B.; Zhang, H.; Liu, Y.; Liu, F.; Lu, S.; Dai, Z. A Feature Extraction Method Using Improved Multi-Scale Entropy for Rolling Bearing Fault Diagnosis. Entropy 2018, 20, 212. https://doi.org/10.3390/e20040212
AMA Style
Ju B, Zhang H, Liu Y, Liu F, Lu S, Dai Z. A Feature Extraction Method Using Improved Multi-Scale Entropy for Rolling Bearing Fault Diagnosis. Entropy. 2018; 20(4):212. https://doi.org/10.3390/e20040212
Chicago/Turabian StyleJu, Bin, Haijiao Zhang, Yongbin Liu, Fang Liu, Siliang Lu, and Zhijia Dai. 2018. "A Feature Extraction Method Using Improved Multi-Scale Entropy for Rolling Bearing Fault Diagnosis" Entropy 20, no. 4: 212. https://doi.org/10.3390/e20040212
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