Journal
ENTROPY
Volume 17, Issue 10, Pages 6683-6697Publisher
MDPI
DOI: 10.3390/e17106683
Keywords
rolling bearing; feature extraction; EEMD; cloud model characteristic entropy
Categories
Funding
- National Natural Science Foundation of China [51107015]
- Science and technology research projects of the Education Department of Hei Long jiang Province [12543057]
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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.
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