Automated Fine-tuning CNN Using Firefly Algorithm for Bearing Fault Diagnostics
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".