Performance Analysis and Comparison of Pre-Trained CNN in Bearing Fault Diagnostics

As deep learning methodologies progress, convolutional neural networks (CNNs) are seeing growing application in image recognition, especially within the domain of bearing fault diagnosis. Utilizing CNN for the automatic identification of features in vibration-related images can enhance both accuracy and efficiency in bearing fault recognition. However, the industry has developed a number of pre trained CNN models and makes it demanding to select models for specific tasks of bearing fault diagnosis. Therefore, this paper aims to compare three of the main-trend CNN models' prediction accuracy and computational performance comprehensively. First, three pretrained CNN models-VGG16, ResNet and SqueezeNet, were trained to classify the bearing vibration signal dataset which had been converted to 2-D scalogram images. Transfer learning was applied to all models in this process. Then, the prediction accuracy and training time were examined and compared. Results showed that the accuracy of all CNN models were acceptable but SqueezeNet has the lowest runtime and highest accuracy. ResNet showed slightly lower performance and VGG16 experienced overfitting and produced the lowest accuracy with the longest runtime. Overall, for similar tasks of image-classification-based bearing fault diagnostics, SqueezeNet is a better candidate compared to other main stream pretrained CNN models, which can bring great accuracy accompanied with better efficiency. The findings of this work are good reference for future model selection in bearing fault diagnosis and related tasks.