Abstract
In order to improve the diagnosis accuracies of the current diagnosis methods, a novel fault diagnosis method of automobile gearbox based on novel successive variational mode decomposition and weighted regularized extreme learning machine is presented for fault diagnosis of gearbox in this paper. The novel successive variational mode decomposition (SVMD) is presented to improve the traditional variational mode decomposition, which finds modes one after the other, and this succession helps increase convergence rate and also not extract the unwanted modes; weighted regularized extreme learning machine (WRELM) is presented to improve the traditional extreme learning machine, which uses the weight of each sample with the nonparametric kernel density estimation and can find the optimal weight for each sample. The test results indicate that the diagnosis accuracy of SVMD-WRELM for gearbox is better than that of VMD-WRELM, VMD-ELM.
Highlights
Gearbox is the key component of automobile, and condition monitoring and fault diagnosis for automobile gearbox are very significant [1,2,3,4]
Us, in order to improve the diagnosis accuracies of the current diagnosis methods, a novel fault diagnosis method based on successive variational mode decomposition and weighted regularized extreme learning machine (SVMDWRELM) is presented for fault diagnosis of automobile gearbox in this paper. e four states of automobile gearbox including normal state, wear and tear, pitting fault, and snaggletooth fault are used in this experiment. e testing results indicate that the diagnosis accuracies of SVMDWRELM for gearbox are better than those of VMDWRELM, VMD-Extreme learning machine (ELM)
It can be seen that SVMD is superior to VMD, and WRELM is superior to ELM. erefore, fault diagnosis for gearbox by SVMDWRELM is feasible
Summary
Gearbox is the key component of automobile, and condition monitoring and fault diagnosis for automobile gearbox are very significant [1,2,3,4]. Artificial neural networks (ANNs), support vector machine (SVM), etc. The feature extraction of the vibrational signal of gearbox has a great influence on the accuracy of fault diagnosis of gearbox. Empirical mode decomposition (EMD), variational mode decomposition (VMD), etc. In order to improve the performance of VMD, Shock and Vibration successive variational mode decomposition (SVMD) finds modes one after the other, and this succession helps increase convergence rate and not extract the unwanted modes. Us, in order to improve the diagnosis accuracies of the current diagnosis methods, a novel fault diagnosis method based on successive variational mode decomposition and weighted regularized extreme learning machine (SVMDWRELM) is presented for fault diagnosis of automobile gearbox in this paper. Us, in order to improve the diagnosis accuracies of the current diagnosis methods, a novel fault diagnosis method based on successive variational mode decomposition and weighted regularized extreme learning machine (SVMDWRELM) is presented for fault diagnosis of automobile gearbox in this paper. e four states of automobile gearbox including normal state, wear and tear, pitting fault, and snaggletooth fault are used in this experiment. e testing results indicate that the diagnosis accuracies of SVMDWRELM for gearbox are better than those of VMDWRELM, VMD-ELM
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