Integrating Spatial and Temporal Features for Bearing Fault Diagnosis
Bibliographic record
Abstract
Bearing failures cause machinery breakdowns, resulting in financial losses due to production downtimes. To address this, accurate bearing condition monitoring is essential. This paper introduces a cross-domain approach to fault diagnosis using a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) models, applied to the Case Western Reserve University (CWRU) dataset and the University of Ottawa Rolling-element Dataset- Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS), which contain both artificial and naturally developed bearing faults. The proposed experimental framework assesses the estimators, training and testing them with raw time-domain data from both acoustic and accelerometer signals, enhancing fault detection across various operating conditions. Results demonstrate that the CNN-LSTM model, when combined with statistical preprocessing, outperforms advanced models in both performance, computational time, and stability, particularly when fusing data from multiple sources. This approach shows promise for practical implementations in industrial predictive maintenance, offering a more reliable solution for reducing downtime and improving operational efficiency. Future work will focus on further optimization of the model and minimizing the data required for effective condition monitoring.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".