Feature learning for bearing prognostics: A comprehensive review of machine/deep learning methods, challenges, and opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| 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 it