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

Abstract

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.

Data mining in predictive maintenance systems: A taxonomy and systematic review

“…Schlagenhauf and Burghardt (2021) proposed an image‐based analysis from the photographs of the degradation of the analyzed surface of a ball screw drive. In Lasisi and Attoh‐Okine (2018) and Consilvio et al (2020), the track geometry measures of railway assets are used, specifically in Consilvio et al (2020), as a complementary source of data together with inspection logs. Other example of images analysis is found in Ullah et al (2017) that takes infrared thermal images of power substations to detect temperature anomalies.…”

Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications

“…Their findings suggest that private and public charging locations offer a similar quality to users, and that there are still some issues that need to be improved in order to expand this mode of sustainable transport. Finally, Consilvio et al [109] discuss the utilization of machine learning methods in railway asset management.…”

Augmented asset management in railways – Issues and challenges in rolling stock

“…[31][32][33][34][35] AM for railways focusses mainly on railway infrastructure such as railway track, 36 interdependencies between railway assets 37 and performance optimization of assets in heavy haul rail. 38 AM for railway infrastructure is based on adoption of various AI techniques like maintenance optimization modelling for railway track by Andrews, [39][40][41][42] Computerized Maintenance Managemtent Systems (CMMS), DSSs, reliability analysis and lifecycle costing for infrastructure, 43 the use of clustering techniques and petri net modelling for intelligent AM for rail earthworks, 44 intelligent AM with decision support for railway signaling system, 45 and intelligent transport system for the European Railway Traffic Management System (ERTMS). 46 Application of AM to Fleet Management (FM) focusses on aspects like reliability of service, ownership, responsibility, performance monitoring and decision making.…”