Special Issue "Sustainable and Trustworthy Operation and Maintenance of Railway Systems"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Prof. Dr. Ramin Karim
E-Mail Website
Guest Editor
Div. of Operation and Maintenance Engineering, Luleå University of Technology, SE-971 87 Luleå, Sweden
Interests: industrial AI and eMaintenance
Prof. Dr. Uday Kumar
E-Mail
Co-Guest Editor
Div. of Operation and Maintenance Engineering, Luleå University of Technology, SE-971 87 Luleå, Sweden
Interests: RAMS; operation and maintenance engineering
Prof. Dr. Diego Galar
E-Mail
Co-Guest Editor
Div. of Operation and Maintenance Engineering, Luleå University of Technology, SE-971 87 Luleå, Sweden
Interests: condition monitoring; industrial AI

Special Issue Information

Dear Colleagues,

With the advent of the 4th Industrial revolution and boarding of the digital train by the railway sector, the implementation of artificial intelligence (AI)-based solutions in railway business has become an important part of strategic thinking of senior railway managers. However, developing and implementing AI-based solutions for enhanced analytics in railway contexts is challenging and requires a good domain understanding and insight in a range of disciplines, such as computer science, data science, system engineering, software engineering, control engineering, statistics, and mathematics.

Today, the railway sector is struggling to identify appropriate approaches when developing AI-based solutions, and at the same time avoiding hype-based implementation to ensure effective and efficient use of resources. However, to strengthen the railway industry’s capability to develop AI-based solutions and boost the implementation of AI tools, we believe that there is a need for fundamental and applied research to study, explore, investigate, and develop frameworks, approaches, technologies, and methodologies that enable operational excellence in the railway though improved fact-based decision making using enhanced analytics empowered by digitalization and AI technologies.

This Special Issue of Sustainability is seeking research findings that focus on the utilization of digitalization and AI technologies that enable sustainable asset management, operation, and maintenance of railway systems, including railway infrastructure and rolling stocks.

The issue welcomes submissions that enable enhanced decision making in managing railway assets with special reference to the operation and maintenance of railways through the use of analytics (i.e., descriptive, diagnostics, prognostics, and prescriptive) based on data and/or physical constraints and other digitalization and AI technologies. The scope of the Special Issue includes a wide range of topics related to the operation and maintenance of railway systems, such as asset management, fleet management, RAMS, maintenance analytics, cybersecurity, data-drive models, predictive maintenance, condition monitoring, machine learning, deep-learning, eMaintenance, digitalization, artificial intelligence, etc.

Prof. Dr. Ramin Karim
Prof. Dr. Uday Kumar
Prof. Dr. Diego Galar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • operation
  • maintenance
  • railway
  • industrial AI
  • asset management
  • eMaintenance
  • condition monitoring
  • analytics
  • asset management
  • fleet management

Published Papers (3 papers)

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Research

Article
Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach
Sustainability 2021, 13(12), 6828; https://doi.org/10.3390/su13126828 - 16 Jun 2021
Cited by 1 | Viewed by 711
Abstract
Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well [...] Read more.
Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system. Full article
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Article
Risk Prioritization for Failure Modes in Mining Railcars
Sustainability 2021, 13(11), 6195; https://doi.org/10.3390/su13116195 - 31 May 2021
Viewed by 679
Abstract
Railway transportation systems are generally used to transport minerals from large-scale mines. Any failure in the railcar components may cause delays or even catastrophic derailment accidents. Failure mode and effect analysis (FMEA) is an effective tool for the risk assessment of mechanical systems. [...] Read more.
Railway transportation systems are generally used to transport minerals from large-scale mines. Any failure in the railcar components may cause delays or even catastrophic derailment accidents. Failure mode and effect analysis (FMEA) is an effective tool for the risk assessment of mechanical systems. This method is an appropriate approach to identify the critical failure modes and provide proper control measures to reduce the level of risk. This research aims to propose an approach to identify and prioritize the failure modes based on their importance degree. To achieve this, the analytical hierarchy process (AHP) is used along with the FMEA. To compensate for the scarcities of the conventional FMEA in using the linguistic variables, the proposed approach is developed under the fuzzy environment. The proposed approach was applied in a case study, a rolling stock operated in an iron ore mine located in Sweden. The results of this study are helpful to identify not only the most important failure modes but also the most serious and critical ones. Full article
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Article
Planning for Railway Station Network Sustainability Based on Node–Place Analysis of Local Stations
Sustainability 2021, 13(9), 4778; https://doi.org/10.3390/su13094778 - 24 Apr 2021
Viewed by 439
Abstract
We principally focus on evaluating the local and entire network performance of railway stations for sustainable logistics management in South Korea. Specifically, we aim to address the issue of dealing with vulnerability in logistics dependent on the degree of connectivity. To resolve this [...] Read more.
We principally focus on evaluating the local and entire network performance of railway stations for sustainable logistics management in South Korea. Specifically, we aim to address the issue of dealing with vulnerability in logistics dependent on the degree of connectivity. To resolve this issue, we investigate (i) the current level of local railway station sustainability performance from the perspectives of the value of the station (node) and the geographical location (place), and (ii) how railway station network management can prepare for imminent internal and external risks. Integrating node–place analysis and social network analysis approaches, we demonstrate a means of assessing (i) local railway station performance by comparing how one station’s value differs from that of other stations, and (ii) overall railway network performance by measuring the degree of connectivity based on the centrality characteristics. Consequently, we recommend improvement in planning orders considering the degree of local performance and network vulnerability for railway station network sustainability. Full article
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