An Integrated Framework for Bearing Fault Diagnosis: Convolutional Neural Network Model Compression Through Knowledge Distillation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".