Optimal Management of the Flow of Parts for Gas Turbines Maintenance by Reinforcement Learning and Artificial Neural Networks

Bellani, Luca; Compare, Michele; Baraldi, Piero; Zio, Enrico

doi:10.1007/978-3-030-89647-8_20

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Simulation environments are therefore more suitable in this case. For instance, authors in [119] developed a TL approach that uses an ANN to learn from state, action, and rewards to output the optimal rewards policy. The algorithms target a sequential RUL prediction process of a specific type of pumping system.…”

Section: Rlmentioning

confidence: 99%

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

Berghout

Benbouzid

2022

Electronics

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Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system’s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.

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Section: Rlmentioning

confidence: 99%

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

Berghout

Benbouzid

2022

Electronics

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“…On the other hand, few works have also investigated RUL extension through mission re-planning. For example, (Camci, Medjaher, Atamuradov, & Berdinyazov, 2019) presents a mathematical formulation for integrated maintenance and mission planning for a fleet of high-value assets, using their current and forecast health information., (Bellani, Compare, Baraldi, & Zio, 2019) investigates the importance of considering the dynamic management of equipment and its influence on future degradation when predicting RUL. It should be noted that this work does not focus on mission re-planning strategy based approaches but envisages control input reconfiguration "on the fly".…”

Section: Introductionmentioning

confidence: 99%

Prognostics Aware Control Design for Extended Remaining Useful Life

Thuillier,

Jha,

Le Martelot

et al. 2024

IJPHM

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As most of the safety critical industrial systems remain sensitive to functional degradation and operate under closed loop, it becomes imperative to take into account the state of health within the control design process. To that end, an effective assessment and extension of the Remaining Useful Life (RUL) of complex systems is a standing challenge that seeks novel solutions at the cross-over of Prognostics and Health Management (PHM) domain as well as automatic control. This paper considers a dynamical system subjected to functional degradation presents a novel control design strategy. Wherein the assessment of state of health of the system is taken into account leading to effective prediction of the RUL as well as its extension. To that end, the degradation model is considered unknown but input-dependent. The control design is formulated as an optimization problem wherein a suitable comprise is reached between the performance and desired RUL of the system. The main contribution of the paper remains in proposal of set-point modulation based approach wherein the control input at a given present time stage is modulated in such way that futuristic health of the system over a long time horizon is extended whilst assuring acceptable performance. The effectiveness of the proposed strategy is assessed in simulation using a numerical example as well as liquid propellant rocket engine case.

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“…While a significant body of work has focused on simulation/optimisation frameworks to solve the maintenance task scheduling problem of a fleet of aircraft [7][8][9][10], in this work we will mainly focus on previous RL applications. Several recent studies [11,12] proposed novel RL frameworks to optimise the O&M of a single mechanical component (e.g., pump/turbine/turbofan engine) and demonstrated that it can find optimal policies when compared to traditional maintenance strategies. However, when considering an entire fleet of aircraft, there is not a single component to optimise but multiple components must be operated simultaneously and cooperatively to reach a common goal, maximising the overall availability of the fleet and minimising the cost of maintaining the fleet.…”

Section: Introductionmentioning

confidence: 99%

Aircraft fleet availability optimisation: a reinforcement learning approach

Vos,

Peng,

Lee

et al. 2023

Aeronaut. j.

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A fleet of aircraft can be seen as a set of degrading systems that undergo variable loads as they fly missions and require maintenance throughout their lifetime. Optimal fleet management aims to maximise fleet availability while minimising overall maintenance costs. To achieve this goal, individual aircraft, with variable age and degradation paths, need to operate cooperatively to maintain high fleet availability while avoiding mechanical failure by scheduling preventive maintenance actions. In recent years, reinforcement learning (RL) has emerged as an effective method to optimise complex sequential decision-making problems. In this paper, an RL framework to optimise the operation and maintenance of a fleet of aircraft is developed. Three cases studies, with varying number of aircraft in the fleet, are used to demonstrate the ability of the RL policies to outperform traditional operation/maintenance strategies. As more aircraft are added to the fleet, the combinatorial explosion of the number of possible actions is identified as a main computational limitation. We conclude that the RL policy has potential to support fleet management operators and call for greater research on the application of multi-agent RL for fleet availability optimisation.

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Optimal Management of the Flow of Parts for Gas Turbines Maintenance by Reinforcement Learning and Artificial Neural Networks

Cited by 3 publications

References 19 publications

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

Prognostics Aware Control Design for Extended Remaining Useful Life

Aircraft fleet availability optimisation: a reinforcement learning approach

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