Convformer-NSE: A Novel End-to-End Gearbox Fault Diagnosis Framework Under Heavy Noise Using Joint Global and Local Information
Bibliographic record
Abstract
The application of convolutional neural network (CNN) has greatly promoted the scope and scenario of intelligent fault diagnosis and brought about a significant improvement of intelligent model performance. Solving the feature extraction and fault diagnosis of machinery with heavy noise is beneficial for stable industrial production. However, the local properties of CNN prevent it from obtaining global features to collect sufficient fault information, leading to the degradation of fault diagnosis performance of CNN under heavy noise. In this article, a novel framework named Convformer-NSE is developed to extract robust features that integrate both global and local information, aiming at improving the end-to-end fault diagnostic performance of gearbox under heavy noise. First, Convformer is constructed to improve the nonlinear representation of the feature map, in which the sparse modified multi self-attention is used to model the long-range dependency of the feature map while keeping attention on local features. Then, the extracted spatial features at various scales are fused and fed in the designed novel Senet (NSE) for channel adaptivity learning. The Convformer-NSE is used for the analysis of raw vibration data of different gearbox systems. The experimental signal analyses demonstrate that our developed framework is superior to others.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".