MSF-DFormer: A Multisensor Multiscale Fusion Network With Deformable Transformer for Fault Diagnosis Under Complex Working Conditions With Limited Samples
Bibliographic record
Abstract
Bearings are critical components in rotating machinery, and their failures could result in significant downtime and safety risks. Intelligent fault diagnosis could encounter considerable challenges in noisy environments and under limited labeled data conditions. A novel multi-sensor multi-scale fusion network with a deformable transformer (MSF-DFormer) is proposed in this paper, which incorporates multi-scale feature extraction, spatial-channel fusion, cross-layer dense enhancement, and a deformable attention mechanism to enhance feature representation across different sensor modalities. Each raw sensor signal is processed through independent cascaded multi-scale depthwise separable convolutional blocks (MDSCB), enabling comprehensive multi-scale feature extraction. The resulting features are refined via a Spatial–Channel Reorganization Module (SCRM) and preliminarily fused using a Sensor Feature Fusion Module (SFFM). Besides, further integration of feature representations from different stages is performed through an adaptive dense feature fusion module (ADFF). Finally, a deformable offset transformer (DOT) module is employed to integrate outputs from multiple stages by dynamically adjusting offsets to capture long-range dependencies and fine-grained local patterns. The experimental results demonstrate that the proposed method achieves an accuracy of 99.78% on the small-sample PU dataset, which contains both vibration and current signals, exhibiting strong diagnostic capability under noisy conditions. On the multi-sensor dataset from the University of Ottawa, which includes both vibration and acoustic signals, the method outperforms several mainstream models, showing superior accuracy, robustness, and generalization performance. These findings confirm that MSF-DFormer performs well in fusing vibration and current signals, as well as in cross-modal fusion scenarios involving vibration and acoustic data, highlighting its versatility and effectiveness across diverse multi-sensor fusion tasks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".