Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization
O Ogunfowora, H Najjaran - Journal of Manufacturing Systems, 2023 - Elsevier
Abstract Systems and machines undergo various failure modes that result in machine health
degradation, so maintenance actions are required to restore them back to a state where they …
degradation, so maintenance actions are required to restore them back to a state where they …
Applications of Reinforcement Learning for maintenance of engineering systems: A review
AP Marugán - Advances in Engineering Software, 2023 - Elsevier
Nowadays, modern engineering systems require sophisticated maintenance strategies to
ensure their correct performance. Maintenance has become one of the most important tasks …
ensure their correct performance. Maintenance has become one of the most important tasks …
A prognostic driven predictive maintenance framework based on Bayesian deep learning
Recent years have witnessed prominent advances in predictive maintenance (PdM) for
complex industrial systems. However, the existing PdM literature predominately separates …
complex industrial systems. However, the existing PdM literature predominately separates …
Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems
Currently, in manufacturing, massive useful data about health condition and maintenance is
often available thanks to Industry 4.0 technologies. However, how to take advantage of …
often available thanks to Industry 4.0 technologies. However, how to take advantage of …
Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
In the context of modern engineering, environmental, and societal concerns, there is an
increasing demand for methods able to identify rational management strategies for civil …
increasing demand for methods able to identify rational management strategies for civil …
A deep reinforcement learning approach for rail renewal and maintenance planning
Develo** optimal rail renewal and maintenance planning that minimizes long-term costs
and risks of failure is of paramount importance for railroad industry. However, intrinsic …
and risks of failure is of paramount importance for railroad industry. However, intrinsic …
A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning
This paper deals with the problem of dynamically integrating tactical production planning
and predictive maintenance in the context of a rolling horizon approach. At the production …
and predictive maintenance in the context of a rolling horizon approach. At the production …
Importance measure-based maintenance strategy optimization: Fundamentals, applications and future directions in AI and IoT
Numerous maintenance strategies have been proposed in the literature related to reliability.
This paper focuses on the utilization of reliability importance measures to optimize …
This paper focuses on the utilization of reliability importance measures to optimize …
[HTML][HTML] Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities
Deep learning algorithms provide plausible benefits for efficient prediction and analysis of
nuclear reactor safety phenomena. However, research works that discuss the critical …
nuclear reactor safety phenomena. However, research works that discuss the critical …
A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies
M Mikhail, MS Ouali, S Yacout - Reliability Engineering & System Safety, 2024 - Elsevier
Optimizing condition-based maintenance (CBM) strategies based on machine learning (ML)
methods as reinforcement learning (RL) have been receiving increasing attention due to …
methods as reinforcement learning (RL) have been receiving increasing attention due to …