Transfer Learning Approach using Simulated Induction Motor Bearing Data: A Comparative Analysis of SE-ResNet and its Hybrid Variants
Bibliographic record
Abstract
Early detection of incipient bearing faults in induction motors has proven crucial in predictive maintenance, helping avoid machine downtime and costly repairs. The main challenge is collecting sufficient data for deep learning models since faults are a rare occurrence. This paper investigates the efficacy of a transfer learning approach for induction motor bearing fault diagnosis using simulated vibration data. Healthy and faulty bearings of different severities were simulated in MATLAB for various noise magnitudes. A Squeeze and Excitation Residual Network (SE-ResNet), previously trained on a large dataset for bearing faults of a Permanent Magnet Synchronous Motor (PMSM), is used as a feature extractor. By leveraging pre-trained knowledge, the model's weights were fine-tuned using Bayesian Optimization, aiming to mitigate the data scarcity issue while maintaining accurate fault classification. The model's performance was compared against three hybrid architectures incorporating Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) layers. The study aims to assess the impact of adding recurrent layers to capture temporal dependencies within simulated vibration signals. Contrary to expectations, the hybrid models did not improve the classification accuracy compared to the standalone pre-trained SE-ResNet. The test accuracy remained the same for all the models at 97.297% whereas the computational cost increased for the hybrid models. This paper analyzes these findings, highlighting the challenges of transfer learning with simulated data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".