Performance Analysis and Comparison of Pre-Trained CNN in Bearing Fault Diagnostics
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".