Feature learning for bearing prognostics: A comprehensive review of machine/deep learning methods, challenges, and opportunities
Bibliographic record
Abstract
• Proposes a taxonomy for feature learning in bearing prognostics, including temporal, spatial, and spatiotemporal methods. • Details imbalanced data handling techniques, including shallow and adversarial methods. • Reviews feature-invariant learning methods to address inconsistent data distributions across varying operational conditions. • Reviews fusion models with integrated feature learning and prediction stages. • Analyzes bearing benchmark datasets, experimental setups, challenges, and proposes future research directions. Mechanical bearings are common elements in a wide range of applications, such as wind turbines and manufacturing. Therefore, bearing prognostics are crucial to preventing catastrophic failures and machinery breakdowns. In this context, extracting the influential features is often the most challenging task in the prognosis process. This complexity arises because of the non-linear and non-stationary nature of the acquired vibration signals. Therefore, this paper offers an extensive examination of state-of-the-art feature-learning methods. Initially, the paper introduces a taxonomy of feature learning methods, encompassing both shallow and deep learning approaches. The paper also discusses methods of feature-learning under imbalanced data samples and different operational settings. Furthermore, the paper details the experimental setups of commonly used benchmark datasets to assist scholars and practitioners in understanding the subject area. Finally, the study discusses the challenges associated with calculating bearings’ RUL and suggests potential areas for further research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".