Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic EntropyReportar como inadecuado




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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





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Academic Editor: Kevin H. Knuth

Abstract 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





Autor: Long Han 1,2, Chengwei Li 1,* and Hongchen Liu 1

Fuente: http://mdpi.com/



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