A New Maintenance Plan for Wind Turbine Farms Using Reinforcement Learning
Bibliographic record
Abstract
This paper addresses the challenge of maintenance planning for multi-component systems, focusing specifically on wind turbine farms, which play a vital role in electricity production. Traditional approaches to maintenance planning rely on pre-specified thresholds, triggering maintenance actions based on conditions such as production rate, system age, or failure states. However, this study proposes a dynamic approach to maintenance planning that considers the actual state of the system in real-time. The system is modeled as a large-scale multi-component parallel production system, where each unit can be in one of three states: good, partial failure, or failure. The transitions between states are governed by a continuous Markov chain, enabling a comprehensive representation of the system's behavior. By utilizing this dynamic modeling approach, maintenance actions can be scheduled based on the current state of the system, allowing for more efficient and effective maintenance decision-making. To optimize the system's profit in an infinite planning horizon, a Markov Decision Process framework is employed. However, due to the exponential increase in the number of system states with the number of units, traditional dynamic programming algorithms are insufficient for solving this large-scale MDP. Hence, reinforcement learning algorithms, specifically Qlearning, are utilized to determine the maintenance actions based on the current system state. The objective of this study is to maximize the system's profit by considering various factors, including the costs of lost demand, profit from overproduction, and the costs associated with maintenance actions. From a practical standpoint, this research holds several values for industries reliant on multi-component production systems. Maintenance managers can harness the insights obtained from this study to formulate cost-effective strategies, ensuring minimal downtime and maximum system uptime. Moreover, as industries progressively lean towards automation and smart manufacturing, the methodologies presented here will be invaluable for integrating AI-driven maintenance protocols
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".