The bearing health in the traction motor is the prerequisite and guarantee for the safe operation of high-speed trains. The vibration signals of the bearing in traction motor feature high nonlinearity, non-stationarity, and background noise. Therefore, the features of the vibration signals are diverse and complex, making it hard to diagnose the faults of the bearing effectively and accurately. To overcome the difficulty, this paper puts forward a novel fault diagnosis method for the bearing of traction motor in high speed trains based on discrete wavelet transform (DWT) and improved deep belief network (DBN). Firstly, the vibration signals were extracted from various faulty bearings, and used to generate a two-dimensional (2D) time–frequency map. Then, the time–frequency map was preprocessed, and subjected to the deep learning (DL) by the improved DBN, aiming to identify the correlation between fault features and fault types. In this way, the fault state of the bearing in the traction motor was diagnosed and identified in a semi-supervised manner. To verify its effectiveness, the proposed method was applied to diagnose the bearing faults of traction motor in high-speed trains through comparative experiments. The results show that our method achieved better diagnosis accuracy than contrastive methods like backpropagation neural network (BPNN) and support vector machine (SVM).

牵引电机的轴承健康是高速列车安全运行的前提和保证。牵引电动机中轴承的振动信号具有高度的非线性，非平稳性和背景噪声。因此，振动信号的特征是多样而复杂的，难以有效，准确地诊断轴承的故障。为克服这一困难，本文提出了一种基于离散小波变换（DWT）和改​​进的深度信念网络（DBN）的高速列车牵引电机轴承故障诊断方法。首先，从各种故障轴承中提取振动信号，并将其用于生成二维（2D）时频图。然后，对时频图进行预处理，并经过改进的DBN进行深度学习（DL），旨在识别故障特征与故障类型之间的相关性。这样，以半监督的方式诊断和识别了牵引电动机中轴承的故障状态。为了验证其有效性，通过比较实验，将所提方法应用于高速列车牵引电机的轴承故障诊断。结果表明，与反向传播神经网络（BPNN）和支持向量机（SVM）的对比方法相比，我们的方法具有更高的诊断准确性。通过对比实验，将该方法用于高速列车牵引电机轴承故障的诊断。结果表明，与反向传播神经网络（BPNN）和支持向量机（SVM）的对比方法相比，我们的方法具有更高的诊断准确性。通过对比实验，将该方法用于高速列车牵引电机轴承故障的诊断。结果表明，与反向传播神经网络（BPNN）和支持向量机（SVM）的对比方法相比，我们的方法具有更高的诊断准确性。