Reliability-based reinforcement learning driven maintenance policy optimization
Why this work is in the frame
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Bibliographic record
Abstract
The study presents a reinforcement learning (RL) method called the Q-learning algorithm to determine the best maintenance policy for equipment. This involves an artificial intelligence agent making decisions and an environment representing the equipment. The agent creates maintenance policies and takes actions, while the environment determines state transitions and rewards based on the actions chosen. The optimization of the maintenance policy starts with predicting equipment reliability using sensor data. This prediction method combines Back Propagation Neural Network (BPNN) algorithms with the Boxing Match Algorithm (BMA), an evolutionary meta-heuristic algorithm. A novel weight update strategy enhances the performance of artificial neural networks in reliability prediction. This integrated model, BMA-BPNN, aims to improve the accuracy of forecasted reliability. The study involves forecasting equipment reliability to determine critical levels, incorporating cost and risk considerations into decision-making for optimizing maintenance policies. As a result, the Q-learning algorithm is used to identify the best maintenance actions based on equipment reliability. Implementing an automated maintenance system that considers equipment reliability and costs can help reduce accidents resulting from maintenance program deficiencies. This study thus contributes to the field by providing an efficient approach to equipment maintenance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it