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Open AccessArticle

Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy

by 1,2, 1,* and 1
1
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
2
School of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Entropy 2015, 17(10), 6683-6697; https://doi.org/10.3390/e17106683
Received: 10 June 2015 / Revised: 16 September 2015 / Accepted: 21 September 2015 / Published: 25 September 2015
The randomness and fuzziness that exist in rolling bearings when faults occur result in uncertainty in acquisition signals and reduce the accuracy of signal feature extraction. To solve this problem, this study proposes a new method in which cloud model characteristic entropy (CMCE) is set as the signal characteristic eigenvalue. This approach can overcome the disadvantages of traditional entropy complexity in parameter selection when solving uncertainty problems. First, the acoustic emission signals under normal and damage rolling bearing states collected from the experiments are decomposed via ensemble empirical mode decomposition. The mutual information method is then used to select the sensitive intrinsic mode functions that can reflect signal characteristics to reconstruct the signal and eliminate noise interference. Subsequently, CMCE is set as the eigenvalue of the reconstructed signal. Finally, through the comparison of experiments between sample entropy, root mean square and CMCE, the results show that CMCE can better represent the characteristic information of the fault signal. View Full-Text
Keywords: rolling bearing; feature extraction; EEMD; cloud model characteristic entropy rolling bearing; feature extraction; EEMD; cloud model characteristic entropy
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MDPI and ACS Style

Han, L.; Li, C.; Liu, H. Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy. Entropy 2015, 17, 6683-6697. https://doi.org/10.3390/e17106683

AMA Style

Han L, Li C, Liu H. Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy. Entropy. 2015; 17(10):6683-6697. https://doi.org/10.3390/e17106683

Chicago/Turabian Style

Han, Long; Li, Chengwei; Liu, Hongchen. 2015. "Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy" Entropy 17, no. 10: 6683-6697. https://doi.org/10.3390/e17106683

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