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.

中文翻译：

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用于汽车变速箱故障诊断的新型连续变分模式分解和加权正则化极限学习机

为了提高当前诊断方法的诊断精度，提出了一种基于新型连续变分模式分解和加权正则化极限学习机的汽车变速箱故障诊断方法。提出了一种新颖的连续变分模式分解（SVMD）算法，以改进传统的变分模式分解方法，这种方法可以一个接一个地找到模式，这种连续操作有助于提高收敛速度，并且不会提取出不需要的模式。提出了加权正则化极限学习机（WRELM）来改进传统的极限学习机，该方法使用每个样本的权重和非参数核密度估计值，可以找到每个样本的最佳权重。