Dynamic Maintenance for a Large Scale Identical Parallel Manufacturing Systems Using Reinforcement Learning
Bibliographic record
Abstract
In this paper, we propose a Machin Learning (ML)-based framework for maintenance decision making for multi-unit system. More specifically, we propose Reinforcement Learning (RL) approach for dynamic maintenance model for multi- component parallel system subject to stochastic degradation and random failures. Deterioration of each unit occurs independently according to a three-state homogenous Markov process such that each unit has three states, namely, healthy, unhealthy, and failure state. The interaction among system states are modeled based on Birth/Birth-Death process. The overall system state is defined based on different combination of individual component state. The optimal maintenance policy for the system is obtained by modeling the problem as Markov Decision Process (MDP) and Q-learning algorithm with focus on cost minimization is applied as a solution methodology. In comparison to tradition MDP approaches, proposed RL solution is more effective and practical in terms of time and cost savings. Specifically, when the state-space of the problem is large, traditional MDP is note capable to converge to the optimal policy in a timely fashion. Therefore, there is a is the decisive need for development of RL-based solution for maintenance decision making. A numerical example is provided which demonstrates how the RL can be used to find the optimal maintenance policy for the system under study.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".