An Integrated Framework for Bearing Fault Diagnosis: Convolutional Neural Network Model Compression Through Knowledge Distillation

The industrial application of rolling bearing fault diagnosis necessitates achieving high classification accuracy while minimizing the number of model parameters to reduce the computational resources and storage space required for the model. To meet this requirement, this study proposes a knowledge distillation convolutional neural network-deep forest (KDCNN-DF) hybrid model framework. The proposed method integrates the continuous wavelet transform (CWT) for signal data processing, a convolutional neural network (CNN) optimized by knowledge distillation (KD) for feature extraction, and a simplified multigranular scanning (MGS) process using deep forest (DF) for fault classification. Besides, during the construction of the student models, this study found that the arrangement order of kernel sizes in the CNN convolutional layers significantly impacts the extraction of bearing fault features. Experimental validation confirmed that architecture with a smaller kernel size preceding a larger kernel size in shallow-level models is more effective. This effect is particularly pronounced after the KD process and adoption in hybrid models, resulting in higher classification accuracy. The proposed KD method reduces the parameter count of the CNN model to 5% of the original number while maintaining relatively high accuracy and significantly reducing computing time. In addition, the modeling architecture of DF has been simplified by adopting a streamlined MGS process. The proposed model achieves the highest accuracy on the original Case Western Reserve University (CWRU) datasets, with 99.75% on the 48-kHz dataset, 99.90% on the 12-kHz dataset, and a perfect 100% on the Ottawa dataset. These results surpass the accuracy of existing methods.