TFDFNet: a dual-branch fault diagnosis model for bearings under noisy and complex industrial environments
Bibliographic record
Abstract
Unexpected failures in rotating machinery can cause costly downtime and safety hazards in industrial systems, highlighting the need for accurate and robust fault diagnosis. However, fault-related signals are often weak and easily obscured by noise, making reliable diagnosis challenging in real-world environments. To address this, we propose Time-frequency and time-series dual-branch fusion network(TFDFNet), a novel dual-branch deep learning model designed to improve fault classification performance under noisy and complex conditions. The model combines two complementary types of information: time-frequency representations derived from continuous wavelet transform and raw time-sequence data captured through sliding-window sampling. A Swin Transformer is used to extract deep features from time-frequency images, while a specially designed module called Gated attention block(GABlock) learns key temporal patterns from the sequence data. These features are fused using a cross-attention mechanism to enhance fault-related information. Extensive experiments on two public bearing fault datasets (CWRU and Ottawa) show that TFDFNet achieves outstanding accuracy, even under severe noise interference. The model reaches up to 100% accuracy on CWRU and 99.44% on Ottawa, and consistently outperforms existing convolutional neural network (CNN) baselines. These results demonstrate the practical potential and robustness of TFDFNet for intelligent fault diagnosis in industrial applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".