Rolling Bearing Fault Diagnosis Based on Acoustic Vibration Signals and Deep Learning
Bibliographic record
Abstract
In industrial equipment, rolling bearings serve as critical rotating components whose health status directly impacts the safety and reliability of mechanical systems. However, in complex industrial environments, factors such as strong noise interference and insufficient single-sensor information pose challenges for capturing fault characteristics. To fully leverage the potential of multi-source information, this paper proposes a feature-level fusion fault diagnosis method based on acoustic-vibration signals and a TCN-Attention network. This method first inputs acoustic and vibration signals into parallel Temporal Convolutional Networks (TCN) to extract multi-scale temporal features. Subsequently, the dual-branch features are concatenated in the channel dimension, and a Channel Attention module is introduced to perform adaptive weight allocation. This approach highlights key modal features, suppresses redundant information, and further enhances deep-layer complementarity. Using the University of Ottawa acoustic-vibration bearing dataset for validation, experimental results demonstrate that the proposed acoustic-vibration fusion model achieves superior fault recognition accuracy and robustness compared to single-modality diagnostic methods.
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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 | 0.001 |
| 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".