This paper suggests label-based, mini-batch methods for convolutional neural network (CNN) based diagnosis of fluid-film bearing rotor systems. Rather than using random mini-batches in the training process, mini-batches are generated based on the label information. Label information is a critical factor for robust diagnosis. Five different types of label-based mini-batches are proposed and their performance is compared to the conventional random mini-batch method. In addition, sensitivity analysis of kernels in convolutional neural networks is suggested as a method to analyze the performance variation. A case study of a fluid-film bearing rotor system is used to show the effect of the proposed methods. The case study results indicate a wide range of performance variation among the proposed mini-batch methods. Of the examined methods, the equally labeled mini-batch approach presents the best performance. Moreover, the results of the kernel sensitivity analysis show that the use of properly sensitive kernels does positively affect the overall performance of the CNN.

本文提出了基于标签的小批量方法，用于基于卷积神经网络 (CNN) 的流体膜轴承转子系统诊断。不是在训练过程中使用随机小批量，而是根据标签信息生成小批量。标签信息是可靠诊断的关键因素。提出了五种不同类型的基于标签的小批量方法，并将它们的性能与传统的随机小批量方法进行了比较。此外，建议将卷积神经网络中内核的敏感性分析作为分析性能变化的一种方法。流体膜轴承转子系统的案例研究用于显示所提出方法的效果。案例研究结果表明，所提出的小批量方法之间存在广泛的性能差异。在检查的方法中，同样标记的小批量方法表现出最佳性能。此外，内核敏感性分析的结果表明，使用适当敏感的内核确实会对 CNN 的整体性能产生积极影响。