Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies

Spigolon, Roberto; Oneto, Luca; Anastasovski, Dimitar; Fabrizio, Nadia; Swiatek, Marie; Canepa, Renzo; Anguita, Davide

doi:10.1007/978-3-030-16841-4_12

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Different techniques of data analysis are already applied in the rail sector, such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF) [22][23][24][25]. Regarding asset management, different studies exist in the literature [26][27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

et al. 2020

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The objective of this study is to show the applicability of machine learning and simulative approaches to the development of decision support systems for railway asset management. These techniques are applied within the generic framework developed and tested within the In2Smart project. The framework is composed by different building blocks, in order to show the complete process from data collection and knowledge extraction to the real-world decisions. The application of the framework to two different real-world case studies is described: the first case study deals with strategic earthworks asset management, while the second case study considers the tactical and operational planning of track circuits’ maintenance. Although different methodologies are applied and different planning levels are considered, both the case studies follow the same general framework, demonstrating the generality of the approach. The potentiality of combining machine learning techniques with simulative approaches to replicate real processes is shown, evaluating the key performance indicators employed within the considered asset management process. Finally, the results of the validation are reported as well as the developed human–machine interfaces for output visualization.

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Section: Literature Reviewmentioning

confidence: 99%

On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

et al. 2020

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“…Data analytic is becoming an important technology within the railway sector [9,10,11,12,13]. With recent advancements in technology, it has become feasible to store and manage a large amount of data to manage, operate and maintain transportation infrastructures through business intelligence [14,15,16].…”

Section: Introductionmentioning

confidence: 99%

Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making

Ejlali¹,

Arian²,

Taghiyeh³

et al. 2023

Preprint

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Revamping structural health monitoring of advanced rail transit systems: A paradigmatic shift from digital shadows to digital twins

Adeagbo,

Wang,

2024

Advanced Engineering Informatics

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Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies

Cited by 3 publications

References 7 publications

On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making

Revamping structural health monitoring of advanced rail transit systems: A paradigmatic shift from digital shadows to digital twins

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