Reinforcement Learning for Rolling Bearing Fault Diagnosis—A Comprehensive Review
Bibliographic record
Abstract
Automatic fault detection and machine diagnosis play a crucial role in preventive maintenance.This study highlights the importance of fault diagnosis in machinery and emphasizes the benefits of preventive and predictive maintenance strategies.The overviews machine and deep learning techniques, and feature extraction methods for automatic fault diagnosis in rolling bearings.The study discusses the challenges machine and deep learning approaches face, including their limited adaptability to different operational conditions and environmental variations.It also suggests reinforcement learning as a potential automatic rolling bearing fault detection solution.The study differentiates between various reinforcement learning methods, including model-based and model-free approaches, and underscores the advantages of deep reinforcement learning.Furthermore, it evaluates several studies that utilized reinforcement learning for feature optimization, parameter optimization, and addressing class imbalance in rolling bearing fault diagnosis.Lastly, the paper summarizes key findings and proposes future research directions, including integrating reinforcement learning with other machine or deep learning methods and developing new algorithms better suited for large datasets and real-time applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".