Diagnosis of Mechanical and Electrical Faults in Electric Machines Using a Lightweight Frequency-Scaled Convolutional Neural Network
Bibliographic record
Abstract
Optimizing computational efficiency while maintaining accuracy in electrical machine fault detection is a critical challenge. To address this, the Frequency-Scaled Convolutional Neural Network is proposed as a lightweight yet highly accurate model for detecting electrical machine faults. A key feature of this model is its initial layer, which is inspired by the effects of faults on frequency harmonics in rotating systems. This layer includes a trainable frequency-scaled convolutional layer, designed to optimally separate frequency features over time, hence, reducing the need for a more complex model to achieve high accuracy. Additionally, to further decrease model complexity, a partially connected 2D linear layer is developed in the final layer. The model's performance is evaluated through three case studies. First, the Case Western Reserve University bearing dataset, a well-established benchmark, is used. Despite having only 2,020 trainable parameters and 190,000 floating point operations per second, compared to other models in the literature with millions of parameters, the proposed model achieves 100% accuracy, significantly reducing computational burden while maintaining precision. The model is also applied to the inter-turn short circuit fault dataset for permanent magnet synchronous motors and a public dataset with various fault types, where it again achieves 100% accuracy.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".