Rolling bearing fault diagnosis based on modified hierarchical multi-mode slope entropy
The feature extraction of rolling bearing vibration signal is usually difficult,and the quality of feature extraction has a great influence on the diagnosis result.In order to improve the accuracy of bearing fault diagnosis,a feature extraction method called modified hierarchical multi-mode slope en-tropy(MHMSE)is proposed and combined with extreme learning machine(ELM)to realize rolling bearing fault diagnosis.MHMSE employs the modified hierarchical method to extract the high and low frequency information of time series.Meanwhile,aiming at the dimension defect of slope entropy(SE),SE is extended to multi-mode slope entropy(MSE)to extract hierarchical component features.Inputting the fault feature vector extracted by MHMSE into the ELM,the bearing faults under nine working conditions can be identified.The experimental results show that the modified hierarchical method is better than the traditional hierarchical and multiscale sequence method.The diagnosis re-sults of MHMSE are better than those of the modified hierarchical permutation entropy(MHPE),re-fined composite multiscale dispersion entropy(RCMDE),refined composite multiscale fuzzy entropy(RCMFE),refined composite multiscale sample entropy(RCMSE),and composite multiscale weigh-ted permutation entropy(CMWPE).
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