Label-based, Mini-batch Combinations Study for Convolutional Neural Network Based Fluid-film Bearing Rotor System Diagnosis

•The effect of using label information in mini-batch generation is studied in this paper.•The composition and the order of mini-batch clearly affects the overall performance of the convolutional neural network diagnosis.•Equal mini-batch method outperforms conventional random mini-batch and other machine learning cases for the imbalanced data set problem.•Overall sensitive kernels based on the proposed kernel sensitivity analysis can estimate the performance of the trained network, accurately.•A case study validates the effect of label-based mini-batch and the sensitivity analysis method. 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.