Prescriptive Maintenance of Railway Infrastructure: From Data Analytics to Decision Support

Consilvio, Alice; Sanetti, Paolo; Anguita, Davide; Crovetto, Carlo; Dambra, Carlo; Oneto, Luca; Papa, Federico; Sacco, Nicola

doi:10.1109/mtits.2019.8883331

Cited by 25 publications

(10 citation statements)

References 26 publications

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“…The goal is to improve i) maintenance activity scheduling, ii) service dependability, and iii) resource utilization and possession times while avoiding (or reducing) contractual fines and delays. [4].…”

Section: Related Workmentioning

confidence: 99%

Proactive Buildings: A Prescriptive Maintenance Approach

Koukaras

Dimara

Gonzalez

et al. 2022

IFIP Advances in Information and Communication Technology

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Prescriptive maintenance has recently attracted a lot of scientific attention. It integrates the advantages of descriptive and predictive analytics to automate the process of detecting non nominal device functionality. Implementing such proactive measures in home or industrial settings may improve equipment dependability and minimize operational expenses. There are several techniques for prescriptive maintenance in diverse use cases, but none elaborates on a general methodology that permits successful prescriptive analysis for small size industrial or residential settings. This study reports on prescriptive analytics, while assessing recent research efforts on multi-domain prescriptive maintenance. Given the existing state of the art, the main contribution of this work is to propose a broad framework for prescriptive maintenance that may be interpreted as a high-level approach for enabling proactive buildings.

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Section: Related Workmentioning

confidence: 99%

Proactive Buildings: A Prescriptive Maintenance Approach

Koukaras

Dimara

Gonzalez

et al. 2022

IFIP Advances in Information and Communication Technology

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“…Different objectives can be considered for prescriptive operation tasks, such as prolonging the remaining useful lifetime and, thereby, improving the reliability and availability of the system; completing a defined mission or reaching an operational goal also in case of adverse conditions or occurred faults; minimizing the emissions or minimizing the energy consumption. Several research studies have recently picked up the direction of prescriptive operation [34,11]. For example, taking the economic and environmental impacts into account and prescribing maintenance operation to improve the efficiency of aircraft maintenance [34].…”

Section: Related Workmentioning

confidence: 99%

A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning

Tian¹,

Han²,

Kulkarni³

et al. 2022

Preprint

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Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing the availability, maintenance and costs of complex systems. Reinforcement learning (RL) algorithms are particularly suitable for this type of problems given their learning capabilities. A special case of a prescriptive operation is the power allocation task, which can be considered as a sequential allocation problem, where the action space is bounded by a simplex constraint. A general continuous action-space solution of such sequential allocation problems has still remained an open research question for RL algorithms. In continuous action-space, the standard Gaussian policy applied in reinforcement learning does not support simplex constraints, while the Gaussiansoftmax policy introduces a bias during training. In this work, we propose the Dirichlet policy for continuous allocation tasks and analyze the bias and variance of its policy gradients. We demonstrate that the Dirichlet policy is bias-free and provides significantly faster convergence, better performance and better hyperparameters robustness over the Gaussiansoftmax policy. Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems. The experimental results show the potential to prescribe optimal operation, improve the efficiency and sustainability of multi-power source systems.

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“…This is a maintenance technique specifically designed to reduce operational costs and asset risk, which aims at collecting and analyzing data concerning the condition of assets to prescribe relevant maintenance actions. To date, researchers have offered some valuable proposals from the management of heterogeneous multimodal asset data [3,4] and maintenance decisions making [5], covering areas such as wind farms [6], aviation industry [7], and residential buildings [8]. Choubey et al [9] presented a holistic end-to-end RxM framework, which integrates maintenance needs analysis, operational data, personnel scheduling, feedback information, and knowledge management.…”

Section: Introductionmentioning

confidence: 99%

Prescriptive maintenance for complex products with digital twin considering production planning and resource constraints

Mao

Liu

Qiu

et al. 2023

Meas. Sci. Technol.

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Maintenance is a critical aspect of complex products through entire life cycle, often requiring coordination of production planning and available resources, while previous studies appear to have rarely addressed. With this in mind, this paper presents a prescriptive maintenance framework based on digital twins for reducing operational risk and maintenance costs of complex equipment clusters. Virtual entities are firstly constructed for each single asset in multiple dimensions, which use real-time or historical sensing data collected from the physical entities to predict the corresponding remaining useful life (RUL). Then such RUL information is incorporated into a stochastic programming model with chance constraints to enable dynamic decision making. In particular, a risk-based optimization model is formulated to take full account of the physical distances between facilities and production gaps. Further, a dual-sense pyramidal transformer model is proposed to sense important details of data in both time and space while capturing temporal dependencies at different scales. We have demonstrated through a maintenance case of turbofan engines that the proposed scheme significantly lowers total maintenance costs while reducing frequent visits from maintenance personnel.

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Prescriptive Maintenance of Railway Infrastructure: From Data Analytics to Decision Support

Cited by 25 publications

References 26 publications

Proactive Buildings: A Prescriptive Maintenance Approach

Proactive Buildings: A Prescriptive Maintenance Approach

A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning

Prescriptive maintenance for complex products with digital twin considering production planning and resource constraints

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