Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks
Bibliographic record
Abstract
Integrated with sensors, processors and RF communication modules, intelligent bearing could achieve the autonomous perception and autonomous decision-making, guarantying the safety and reliability during their use. However, because of the resource limitations of the end device, processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network (CNN), which involves a great amount of multiplicative operations. To minimize the computation cost of the conventional CNN, based on the idea of AdderNet, a 1-D adder neural network with a wide first-layer kernel (WAddNN) suitable for bearing fault diagnosis is proposed in this paper. The proposed method uses the l1-norm distance between filters and input features as the output response, thus making the whole network almost free of multiplicative operations. The whole model takes the original signal as the input, uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise, then uses two layers of small kernels for nonlinear mapping. Through experimental comparison with CNN models of the same structure, WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost. The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".