The increasing reliability and availability requirements of power electronic systems have drawn great concern in many industrial applications. Aiming at the difficulty in fault characteristics extraction and fault modes classification of the three-phase full-bridge inverter (TFI) that used as the drive module of brushless DC motor (BLDCM). A hybrid convolutional neural network (HCNN) model consists of one-dimensional CNN (1D-CNN) and two-dimensional CNN (2D-CNN) is proposed in this paper, which can tap more effective spatial feature for TFI fault diagnosis. The frequency spectrum from the three-phase current signal preprocess are applied as the input for 1D-CNN and 2D-CNN to conduct feature extraction, respectively. Then, the feature layers information are combined in the fully connected layer of HCNN. Finally, the performance status of TFI could be identified by the softmax classifier with Adam optimizer. Several groups of experiments have been studied when the BLDCM under different operating conditions. The results show that the fusion features can get a higher degree of discrimination so as to the presented network model also obtains better classification accuracy, which verify the feasibility and superiority to the other networks.

电力电子系统对可靠性和可用性的日益提高的需求已在许多工业应用中引起了极大的关注。针对三相无刷直流电动机（BLDCM）驱动模块三相全桥逆变器（TFI）的故障特征提取和故障模式分类的难题。提出了一种由一维CNN（1D-CNN）和二维CNN（2D-CNN）组成的混合卷积神经网络模型，该模型可以利用更有效的空间特征进行TFI故障诊断。来自三相电流信号预处理的频谱分别用作1D-CNN和2D-CNN的输入以进行特征提取。然后，将特征层信息合并到HCNN的完全连接层中。最后，TFI的性能状态可以通过使用Adam优化器的softmax分类器进行识别。当BLDCM在不同的操作条件下时，已经研究了几组实验。结果表明，融合特征可以得到较高的区分度，从而使所提出的网络模型也具有较好的分类精度，从而证明了其与其他网络的可行性和优越性。