Traditional fault diagnosis methods of DC (direct current) motors require high expertise and human labor. However, the other disadvantages of these methods are low efficiency and poor accuracy. To address these problems, a new adaptive and intelligent mechanical fault diagnosis method for DC motors based on variational mode decomposition (VMD), singular value decomposition (SVD), and residual deep convolutional neural networks with wide first-layer kernels (R-WDCNN) was proposed. First, the vibration signals of a DC motor were collected by a designed acquisition system. Subsequently, VMD was employed to decompose the raw signals adaptively into several intrinsic mode functions (IMFs). Moreover, the transient frequency means method, which can quickly and accurately obtain the optimal value of K, is proposed. SVD was applied to reduce the dimensionality of the IMF matrix for further feature extraction. Finally, the reconstructed matrix containing the main fault feature information was used to train and test the R-WDCNN. Based on residual learning, identification and classification of four types of vibration signals were achieved, while the R-WDCNN was optimized by the adaptive batch normalization algorithm (AdaBN). The recognition rate and the convergence were improved by this classifier. The results show that the method proposed in this paper has better adaptability and intelligence than other methods, and the R-WDCNN can reach a 94% recognition rate on unknown samples. Therefore, the proposed method is more intelligent and accurate than other methods.

DC（直流）电动机的传统故障诊断方法需要很高的专业知识和人力。但是，这些方法的其他缺点是效率低和准确性差。为了解决这些问题，基于变分模式分解（VMD），奇异值分解（SVD）和具有宽第一层内核的残差深度卷积神经网络（R-WDCNN）的直流电动机自适应和智能机械故障诊断新方法被提出。首先，通过设计的采集系统采集直流电动机的振动信号。随后，使用VMD将原始信号自适应地分解为几个固有模式函数（IMF）。此外，提出了一种瞬时频率均值方法，该方法可以快速，准确地获得最优的K值。SVD用于减少IMF矩阵的维数以进一步提取特征。最后，将包含主要故障特征信息的重构矩阵用于训练和测试R-WDCNN。在残差学习的基础上，实现了四种振动信号的识别和分类，并通过自适应批归一化算法（AdaBN）对R-WDCNN进行了优化。该分类器提高了识别率和收敛性。结果表明，本文提出的方法具有比其他方法更好的适应性和智能性，并且R-WDCNN在未知样本上的识别率达到94％。因此，所提出的方法比其他方法更加智能和准确。包含主要故障特征信息的重建矩阵用于训练和测试R-WDCNN。在残差学习的基础上，实现了四种振动信号的识别和分类，并通过自适应批归一化算法（AdaBN）对R-WDCNN进行了优化。该分类器提高了识别率和收敛性。结果表明，本文提出的方法具有比其他方法更好的适应性和智能性，并且R-WDCNN在未知样本上的识别率达到94％。因此，所提出的方法比其他方法更加智能和准确。包含主要故障特征信息的重构矩阵用于训练和测试R-WDCNN。在残差学习的基础上，实现了四种振动信号的识别和分类，并通过自适应批归一化算法（AdaBN）对R-WDCNN进行了优化。该分类器提高了识别率和收敛性。结果表明，本文提出的方法具有比其他方法更好的适应性和智能性，并且R-WDCNN在未知样本上的识别率达到94％。因此，所提出的方法比其他方法更加智能和准确。而R-WDCNN是通过自适应批量归一化算法（AdaBN）进行优化的。该分类器提高了识别率和收敛性。结果表明，本文提出的方法具有比其他方法更好的适应性和智能性，并且R-WDCNN在未知样本上的识别率达到94％。因此，所提出的方法比其他方法更加智能和准确。而R-WDCNN是通过自适应批量归一化算法（AdaBN）进行优化的。该分类器提高了识别率和收敛性。结果表明，本文提出的方法具有比其他方法更好的适应性和智能性，并且R-WDCNN在未知样本上的识别率达到94％。因此，所提出的方法比其他方法更加智能和准确。