| XIE Zhong-min，SHEN Bao-guo，HU Chao.Bearing Fault Diagnosis Method based on EMD-ICA and Genetic Algorithms[J].航空发动机,2021,47(5):34-40 |
| Bearing Fault Diagnosis Method based on EMD-ICA and Genetic Algorithms |
| DOI： |
| Key Words:rolling bearing faults diagnosis vibration signal genetic algorithm EMD independent component |
|
| Hits: 1395 |
| Download times: 1023 |
| Abstract:In view of the problem that the bearing fault characteristic signal in the vibration signal is weak and difficult to identify，
this paper deals with the inner loop fault，the outer loop fault and the rolling element fault vibration signal collected by the laboratory.The
vibration signal is preprocessed by least squares method and exponential smoothing method. And local characteristics of the vibration signal
are separated by EMD，and the reconstruction is realized according to the information entropy gain ratio of the IMF component. The hybrid
vibration signal is separated by ICA，and the feature extraction of the separated vibration signal is carried out. The genetic algorithm is
used to reduce the dimension of the vibration characteristic parameters，and the optimal characteristic parameters are selected. Finally，
the bearing fault vibration feature set is identified by the extreme learning machine optimized by genetic algorithm，and the common SVM
and BP are used as comparison algorithms. The experimental results show that ICA can effectively separate the mixed signals，so as to realize
the extraction of more faulty features. The genetic algorithm can not only achieve the optimal feature selection，but also extreme learning
machine optimized by genetic algorithm compared with other diagnostic methods，which can make the average diagnosis effect of three
kinds of faults reach more than 90%. |
| View Full Text View/Add Comment Download reader |
