Application of EHDE and WHO-SVM model in gearbox fault diagnosis
Aiming at the defect that the existing gear box fault diagnosis methods were sensitive to data length,a gearbox fault diagnosis model based on enhanced hierarchical diversity entropy(EHDE)and wild horse optimizer(WHO)optimized support vector machine(SVM)was proposed.Firstly,the traditional entropy value feature extraction method was sensitive to the length of the data sample in the feature extraction stage,so the enhanced hierarchical diversity entropy method was proposed and used as a feature extraction index to extract the fault features of the gearbox.Secondly,the WHO algorithm was used to optimize the parameters of SVM model,and a WHO-SVM classifier with optimal parameters was established.Finally,the fault feature samples were input to WHO-SVM classifierfor training and test,and the fault identification of the samples was completed.By using gearbox data set,EHDE,refined composite multiscale sample entropy,refined composite multiscale fuzzy entropy,refined composite multiscale permutation entropy,refined composite multiscale dispersion entropy and refined composite multiscale fluctuation dispersion entropy were compared from three perspectives:data length sensitivity,algorithm feature extraction time and model diagnosis performance,respectively.The research results show that EHDE method has low requirements on data length,and can achieve 99.1%average recognition accuracy when the data length is 512,which is superior to other comparison methods in terms of diagnostic stability and diagnostic accuracy.In the generalization experiment of the algorithm,EHDE method can identify the different fault type of the gearbox with 98%accuracy,which has obvious generalization and universality.
NETL
NSTL
万方数据
