With the rapid development of urban rail transit in recent years, it becomes necessary to ensure the operation safety of train wheelset axle boxes. Aiming at the problems of large model size and long diagnosis time in traditional fault diagnosis methods, this paper proposed a novel model to identify concurrent faults in wheelset axle boxes based on an efficient neural network and the attention mechanism. The model was developed based on an improved Ghost bottleneck module sequence to achieve an efficient model. Furthermore, the use of the convolutional block attention module adaptively refined the feature map to improve the generalization of the model. Feature pyramid network was used to fuse the shallow and deep features in the network to improve the extraction ability of various size features. Experiments were carried out on wheelset axle box concurrent fault datasets. Model size, diagnosis speed and accuracy were used as evaluation indexes to compare the efficiencies of existing fault diagnosis methods and our proposed model. Experimental results revealed that the proposed algorithm effectively diagnosed wheelset axle box concurrent faults.

近年来，随着城市轨道交通的迅猛发展，有必要确保火车轮对轴箱的运行安全。针对传统故障诊断方法模型规模大，诊断时间长的问题，提出了一种基于高效神经网络和注意机制的轮毂轴箱并发故障识别模型。该模型是基于改进的Ghost瓶颈模块序列开发的，以实现有效的模型。此外，使用卷积块注意模块自适应地改进了特征图，以提高模型的通用性。特征金字塔网络用于融合网络中的浅层特征和深层特征，以提高各种尺寸特征的提取能力。对轮对轴箱并发故障数据集进行了实验。使用模型大小，诊断速度和准确性作为评估指标，比较现有故障诊断方法和我们提出的模型的效率。实验结果表明，该算法可以有效地诊断轮对轴箱并发故障。