Reinforcement Learning based on Stochastic Dynamic Programming for Condition-based Maintenance of Deteriorating Production Processes
Bibliographic record
Abstract
In this paper, a stochastic dynamic programming model is developed for maintenance planning on a deteriorating multistate production system. The quality of the bath/lot of items produced in each stage is employed as a condition monitoring for condition-based maintenance. The machine has m-1 operational states plus a non-operational state referred as the failure state. At the start of each stage, four actions are available for the management: (1) renew the system; (2) implement maintenance; (3) continue the production, and (4) inspect the system. It is assumed that the impact of the maintenance is imperfect which means after the maintenance, the system is restored to any non-worse states with known probabilities. As the system states change Markovianlly at the end of each stage, and the quality of the items produced depends on the system state, the system can be modeled based on a Markov decision process (MDP). As the MDP is the core of reinforcement learning, for the large-scale problem, it is discussed that the proposed stochastic dynamic programming can be employed to develop reinforcement learning algorithms. To this end, Q-learning algorithm is proposed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".