[HTML][HTML] Maintenance optimization in industry 4.0
This work reviews maintenance optimization from different and complementary points of
view. Specifically, we systematically analyze the knowledge, information and data that can …
view. Specifically, we systematically analyze the knowledge, information and data that can …
A review of predictive and prescriptive offshore wind farm operation and maintenance
Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as
they continue to grow in scale and capacity, so does the requirement for their efficient and …
they continue to grow in scale and capacity, so does the requirement for their efficient and …
Wind farm control technologies: from classical control to reinforcement learning
H Dong, J Xie, X Zhao - Progress in Energy, 2022 - iopscience.iop.org
Wind power plays a vital role in the global effort towards net zero. A recent figure shows that
93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year …
93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year …
Comparative analysis of offshore wind turbine blade maintenance: RL-based and classical strategies for sustainable approach
AP Hendradewa, S Yin - Reliability Engineering & System Safety, 2025 - Elsevier
This study compares traditional methods like Corrective Maintenance (CM), Scheduled
Maintenance (SM), and Condition-based Maintenance (CbM) with Reinforcement Learning …
Maintenance (SM), and Condition-based Maintenance (CbM) with Reinforcement Learning …
Deep reinforcement learning based on proximal policy optimization for the maintenance of a wind farm with multiple crews
The life cycle of wind turbines depends on the operation and maintenance policies adopted.
With the critical components of wind turbines being equipped with condition monitoring and …
With the critical components of wind turbines being equipped with condition monitoring and …
[HTML][HTML] Monte carlo tree search-based deep reinforcement learning for flexible operation & maintenance optimization of a nuclear power plant
Nuclear power plants (NPPs) are required to operate on a flexible profitable production plan
while guaranteeing high safety standards. Deep reinforcement learning (DRL) is an effective …
while guaranteeing high safety standards. Deep reinforcement learning (DRL) is an effective …
[HTML][HTML] A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for …
Abstract The Operation & Maintenance (O&M) of Cyber-Physical Energy Systems (CPESs) is
driven by reliable and safe production and supply, that need to account for flexibility to …
driven by reliable and safe production and supply, that need to account for flexibility to …
[HTML][HTML] Flexible operation and maintenance optimization of aging cyber-physical energy systems by deep reinforcement learning
Abstract Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware
components to ensure a reliable and safe physical power production and supply …
components to ensure a reliable and safe physical power production and supply …
Agent-based modeling and reinforcement learning for optimizing energy systems operation and maintenance: the Pathmind solution
The optimization of the Operation and Maintenance (O&M) of energy systems equipped with
Prognostics and Health Management (PHM) capabilities can be framed as a sequential …
Prognostics and Health Management (PHM) capabilities can be framed as a sequential …
Maintenance policies optimization in the Industry 4.0 paradigm
M Urbani - 2021 - lutpub.lut.fi
Maintenance management is a relevant issue in modern technical systems due to its
financial, safety, and environmental implications. The need to rely on physical assets makes …
financial, safety, and environmental implications. The need to rely on physical assets makes …