A light-weight factorized convolutions based dual-input fuzzy-CNN for efficient motor bearing fault diagnosis
Bibliographic record
Abstract
Efficient and timely identification of bearing faults is imperative to ensure operational normalcy, reduced down-times and health hazards in motor fault tolerant control systems. This paper proposes a fault diagnosis method that combines the vibration information with time-varying rotational speed for effective fault diagnosis under non-stationary conditions. The complex wavelet transform is used to encode both the vibrational and rotational signals in 2d representations for spatial feature extraction. An efficient algorithm is proposed to select the mother wavelet with the least average entropy. Moreover, a spatial decomposition-based approach using factorised convolutions is used to create a light-weight fuzzy convolutional neural network named Split-Operation Fuzzy Convolutional Neural Network (SOF-CNN) for semi rule-based feature extraction and classification. The performance was evaluated on the University of Ottawa (UOO) dataset for multiple speed conditions and cross-condition validation with the highest accuracy yield being 99.92% from the fourth condition and 3rd trial acquired via averaged 10-Fold Cross Validation. The average accuracy yield across all the scenario was 99.89% with 64.73% being the highest accuracy for cross-condition validation. The performance was evaluated across a diverse range of evaluation criterion including both quantitative and statistical tests.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".