There exist many rotating machinery parts, and many types of failure modes, including single failure modes and compound failure modes. This brings high requirements on the performance and generalization ability of fault diagnosis methods. Compared with single fixed model, ensemble model can gather the strengths of others to achieve more accurate identification performance and stronger generalization ability. Based on this, a novel method called ensemble adaptive batch-normalized convolutional neural networks is proposed for rotating machinery fault diagnosis. Firstly, batch normalization and exponentially decaying learning rate are applied to basic convolutional neural network to address internal covariate shift problem, and achieve better diagnostic results and faster convergence speed. Secondly, a series of adaptive batch-normalized convolutional neural networks with different properties are designed. Thirdly, K-fold cross validation is utilized to train all models and parameter transfer is adopted to save computing time. Finally, a new combination strategy is proposed to efficiently ensemble the diagnosis results of all models. The proposed method is demonstrated by practical locomotive bearing dataset and extensive experiments.

存在许多旋转机械零件，并且故障类型很多，包括单一故障模式和复合故障模式。这对故障诊断方法的性能和泛化能力提出了很高的要求。与单个固定模型相比，集成模型可以集合其他模型的优势，以实现更准确的识别性能和更强的泛化能力。在此基础上，提出了一种适用于旋转机械故障诊断的新方法-集成自适应批量归一化卷积神经网络。首先，将批量归一化和指数衰减学习率应用于基本卷积神经网络，以解决内部协变量偏移问题，并获得更好的诊断结果和更快的收敛速度。其次，设计了一系列具有不同性质的自适应批量归一化卷积神经网络。第三，利用K折交叉验证来训练所有模型，并采用参数传递来节省计算时间。最后，提出了一种新的组合策略来有效地整合所有模型的诊断结果。通过实际的机车轴承数据集和广泛的实验证明了该方法。