Smart Maintenance Optimization for Large Scale Parallel Systems Using Deep Reinforcement Learning
Bibliographic record
Abstract
In today's era of Industry 4.0, with the unprecedented availability of data and advancements in technology, it is imperative to adopt smart and dynamic maintenance scheduling, especially for large-scale systems, to harness optimal operational efficiency. In this regard, this article presents a machine learning-based maintenance decision-making framework for multiunit systems. Specifically, we apply deep reinforcement learning (DRL) to a dynamic maintenance model designed for a multiunit parallel system subject to stochastic degradation and random failures. Each unit deteriorates independently through a three-state homogeneous Markov process, transitioning between healthy, unhealthy, or failed states. We define the overall system state by combining individual component states and model their interactions using the bivariate birth/birth–death process. To minimize costs, we use the Markov decision process framework to solve the optimal maintenance policy. We evaluate and compare advanced DRL methods, including proximal policy optimization (PPO) and double deep Q-networks (DDQN), against several baseline approaches. The results show that PPO consistently outperforms all methods, providing the most effective and reliable maintenance strategies. While DDQN performs better than some baseline methods, it occasionally falls short compared to others. These findings highlight the strengths and limitations of different reinforcement learning techniques in determining optimal maintenance policies. In addition, we provide a numerical example that illustrates the use of reinforcement learning methods in a practical scenario, emphasizing the scalability and efficiency of our proposed framework for large-scale systems. Our results show exemplary performance in optimizing maintenance strategies and contribute to the advancement of smart maintenance solutions for complex industrial systems.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".