Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy
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 si...
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doaj-63bdb4562ad445c7af1c7469470c459f2020-11-25T01:23:03ZengMDPI AGEntropy1099-43002015-09-0117106683669710.3390/e17106683e17106683Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic EntropyLong Han0Chengwei Li1Hongchen Liu2School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaThe 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.http://www.mdpi.com/1099-4300/17/10/6683rolling bearingfeature extractionEEMDcloud model characteristic entropy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Long Han Chengwei Li Hongchen Liu |
spellingShingle |
Long Han Chengwei Li Hongchen Liu Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy Entropy rolling bearing feature extraction EEMD cloud model characteristic entropy |
author_facet |
Long Han Chengwei Li Hongchen Liu |
author_sort |
Long Han |
title |
Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy |
title_short |
Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy |
title_full |
Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy |
title_fullStr |
Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy |
title_full_unstemmed |
Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy |
title_sort |
feature extraction method of rolling bearing fault signal based on eemd and cloud model characteristic entropy |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2015-09-01 |
description |
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. |
topic |
rolling bearing feature extraction EEMD cloud model characteristic entropy |
url |
http://www.mdpi.com/1099-4300/17/10/6683 |
work_keys_str_mv |
AT longhan featureextractionmethodofrollingbearingfaultsignalbasedoneemdandcloudmodelcharacteristicentropy AT chengweili featureextractionmethodofrollingbearingfaultsignalbasedoneemdandcloudmodelcharacteristicentropy AT hongchenliu featureextractionmethodofrollingbearingfaultsignalbasedoneemdandcloudmodelcharacteristicentropy |
_version_ |
1725123915737464832 |