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Sustainable and Trustworthy Operation and Maintenance of Railway Systems

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 21403

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Special Issue Editors

Prof. Dr. Ramin Karim

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Guest Editor

Division of Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, SE-971 87 Luleå, Sweden
Interests: industrial AI and eMaintenance

Prof. Dr. Uday Kumar

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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

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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 submissions that pass pre-check are 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 2400 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 (7 papers)

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Research

19 pages, 4611 KiB

Open AccessArticle

A Human-Centric Model for Sustainable Asset Management in Railway: A Case Study

by Ravdeep Kour, Miguel Castaño, Ramin Karim, Amit Patwardhan, Manish Kumar and Rikard Granström

Sustainability 2022, 14(2), 936; https://doi.org/10.3390/su14020936 - 14 Jan 2022

Cited by 3 | Viewed by 2531

Abstract

The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway. Full article

(This article belongs to the Special Issue Sustainable and Trustworthy Operation and Maintenance of Railway Systems)

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16 pages, 4069 KiB

Open AccessArticle

Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks

by Pablo Garrido Martínez-Llop, Juan de Dios Sanz Bobi, Álvaro Solano Jiménez and Jorge Gutiérrez Sánchez

Sustainability 2021, 13(21), 12265; https://doi.org/10.3390/su132112265 - 6 Nov 2021

Cited by 3 | Viewed by 2205

Abstract

Recently, passenger comfort and user experience are becoming increasingly relevant for the railway operators and, therefore, for railway manufacturers as well. The main reason for this to happen is that comfort is a clear differential value considered by passengers as final customers. Passengers’ comfort is directly related to the accelerations received through the car-body of the train. For this reason, suspension and damping components must be maintained in perfect condition, assuring high levels of comfort quality. An early detection of any potential failure in these systems derives in a better maintenance inspections’ planification and in a more sustainable approach to the whole train maintenance strategy. In this paper, an optimized model based on neural networks is trained in order to predict lateral car-body accelerations. Comparing these predictions to the values measured on the train, a normal characterisation of the lateral dynamic behaviour can be determined. Any deviation from this normal characterisation will imply a comfort loss or a potential degradation of the suspension and damping components. This model has been trained with a dataset from a specific train unit, containing variables recorded every second during the year 2017, including lateral and vertical car-body accelerations, among others. A minimum average error of 0.034 m/s² is obtained in the prediction of lateral car-body accelerations. This means that the average error is approximately 2.27% of the typical maximum estimated values for accelerations in vehicle body reflected in the EN14363 for the passenger coaches (1.5 m/s²). Thus, a successful model is achieved. In addition, the model is evaluated based on a real situation in which a passenger noticed a lack of comfort, achieving excellent results in the detection of atypical accelerations. Therefore, as it is possible to measure acceleration deviations from the standard behaviour causing lack of comfort in passengers, an alert can be sent to the operator or the maintainer for a non-programmed intervention at depot (predictive maintenance) or on board (prescriptive maintenance). As a result, a condition-based maintenance (CBM) methodology is proposed to avoid comfort degradation that could end in passenger complaints or speed limitation due to safety reasons for excessive acceleration. This methodology highlights a sustainable maintenance concept and an energy efficiency strategy. Full article

(This article belongs to the Special Issue Sustainable and Trustworthy Operation and Maintenance of Railway Systems)

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21 pages, 5285 KiB

Open AccessArticle

Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

by Erum Saba, Imtiaz Hussain Kalwar, Mukhtiar Ali Unar, Abdul Latif Memon and Nasrullah Pirzada

Sustainability 2021, 13(18), 10249; https://doi.org/10.3390/su131810249 - 14 Sep 2021

Cited by 4 | Viewed by 3001

Abstract

The deterioration of railway wheel tread causes unexpected breakdowns with increasing risk of operational failure leading to higher maintenance costs. The timely detection of wheel faults, such as wheel flats and false flanges, leading to varying conicity levels, helps network operators schedule maintenance before a fault occurs in reality. This study proposes a multiple model-based novel technique for the detection of railway wheelset conicity. The proposed idea is based on an indirect method to identify the actual conicity condition by analyzing the lateral acceleration of the wheelset. It in fact incorporates a combination of multiple Kalman filters, tuned on a particular conicity level, and a fuzzy logic identification system. The difference between the actual conicity and its estimated version from the filters is calculated, which provides the foundation for further processing. After preprocessing the residuals, a fuzzy inference system is used that identifies the actual conicity of the wheelset by assessing the normalized rms values from the residuals of each filter. The proposed idea was validated by simulation studies to endorse its efficacy. Full article

(This article belongs to the Special Issue Sustainable and Trustworthy Operation and Maintenance of Railway Systems)

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19 pages, 671 KiB

Open AccessArticle

Distributed Ledger for Cybersecurity: Issues and Challenges in Railways

by Amit Patwardhan, Adithya Thaduri and Ramin Karim

Sustainability 2021, 13(18), 10176; https://doi.org/10.3390/su131810176 - 12 Sep 2021

Cited by 5 | Viewed by 2631

Abstract

The railway is a complex technical system of systems in a multi-stakeholder environment. The implementation of digital technologies is essential for achieving operational excellence and addressing stakeholders’ needs and requirements in relation to the railways. Digitalization is highly dependent on an appropriate digital infrastructure provided through proper information logistics, whereas cybersecurity is critical for the overall security and safety of the railway systems. However, it is important to understand the various issues and challenges presented by governance, business, and technical requirements. Hence, this paper is the first link in the chain to explore, understand, and address such requirements. The purpose of this paper is to identify aspects of distributed ledgers and to provide a taxonomy of issues and challenges to develop a secure and resilient data sharing framework for railway stakeholders. Full article

(This article belongs to the Special Issue Sustainable and Trustworthy Operation and Maintenance of Railway Systems)

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18 pages, 2946 KiB

Open AccessArticle

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

by Antonio Gálvez, Alberto Diez-Olivan, Dammika Seneviratne and Diego Galar

Sustainability 2021, 13(12), 6828; https://doi.org/10.3390/su13126828 - 16 Jun 2021

Cited by 19 | Viewed by 4478

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 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

(This article belongs to the Special Issue Sustainable and Trustworthy Operation and Maintenance of Railway Systems)

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14 pages, 2253 KiB

Open AccessArticle

Risk Prioritization for Failure Modes in Mining Railcars

by Mohammad Javad Rahimdel and Behzad Ghodrati

Sustainability 2021, 13(11), 6195; https://doi.org/10.3390/su13116195 - 31 May 2021

Cited by 4 | Viewed by 2833

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. 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

(This article belongs to the Special Issue Sustainable and Trustworthy Operation and Maintenance of Railway Systems)

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12 pages, 2481 KiB

Open AccessArticle

Planning for Railway Station Network Sustainability Based on Node–Place Analysis of Local Stations

by Joon-Seok Kim and Nina Shin

Sustainability 2021, 13(9), 4778; https://doi.org/10.3390/su13094778 - 24 Apr 2021

Cited by 6 | Viewed by 2174

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 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

(This article belongs to the Special Issue Sustainable and Trustworthy Operation and Maintenance of Railway Systems)

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