Bearing Fault Diagnosis Using Domain Adaptation Approach for Acoustic Data
Bibliographic record
Abstract
Bearing failures are one of the most occurring problems in industrial machines. Thus, bearings require improved fault detection methods. In this direction, data-driven approaches for machine fault diagnosis have proven to be more effective than the model-based approaches. However, conventional data-driven methods in domain shift conditions are unable to yield optimal performance. Bearing faults usually occur under different operational conditions. Related to this, acoustic emissions as a non-invasive can capture valuable information about machine health conditions and it is considered as an effective alternative to vibration and current-based methods. Moreover, the application of acoustic data in the domain-shift scenario has not been much explored. In this research, we implement a transfer learning approach for bearing fault diagnosis using machine acoustic data while considering the domain shift problem. Three deep learning models including 1DCNN, 1DCNN-LSTM, and a Residual network are developed and investigated in this research. The pre-trained models are implemented based on the DCASE dataset. The pre-trained models are established using the Air Compressor dataset. By using transfer learning, the feature parameters obtained during model development on the Air compressor dataset are utilized to fine-tune the model on the DCASE dataset. The results demonstrate that the model accuracy through the proposed approach is improved to 89.7% for the target domain. The hybrid model 1DCNN-LSTM demonstrated the best results than the other two algorithms.
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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 | 0.000 | 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 | 0.001 | 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".