Automated Fine-tuning CNN Using Firefly Algorithm for Bearing Fault Diagnostics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automated fine-tuning of Convolutional Neural Networks (CNNs) is essential for improving diagnostic accuracy in bearing fault detection. Traditional methods often require manual tuning of hyperparameters, or exhaustively searches through all combinations of hyperparameters, which can be time-consuming and suboptimal, especially in complex fault scenarios. In this work, a novel approach is presented that integrates the Firefly Algorithm (FA) with CNNs to automate the fine-tuning process, optimizing key hyperparameters such as batch size, units, epochs and learning rates. The Firefly Algorithm, inspired by the natural behavior of fireflies, excels in exploring the search space for global optima, making it well-suited for optimizing CNN architectures. Applied to MFPT data, the proposed method demonstrates extraordinary adaptability in various of CNN models and also presented improvements in test accuracy and computational efficiency comparing to main-stream automated finetuning approaches. This framework provides a scalable solution for deploying CNN-based diagnostic systems across various industrial applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it