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Machines
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Railway Vehicle Maintenance 4.0
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Railway Vehicle Maintenance 4.0

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6813

Special Issue Editors

Prof. Dr. Gareth Tucker

E-Mail Website
Guest Editor
Institute of Railway Research, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: rolling stock maintenance; robotics; RCM & prognostics; operations management; machine learning; vehicle-track interaction
Prof. Dr. Simon Iwnicki

E-Mail Website
Guest Editor
Institute of Railway Research, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: railway vehicle dynamics; vehicle-track interaction; computer simulation; running gear design

Special Issue Information

Dear Colleagues,

In recent years, a range of industries, including manufacturing, aviation and construction, have benefited from developments in increased use of automation, use of modern sensors and data analytics, the Internet of Things (IoT) and process optimization based on machine learning. These developments have been associated with the ‘4th Industrial Revolution’. There is a significant opportunity to apply some of the same technologies and techniques to improve the efficiency of railway vehicle maintenance. This already starting to occur, especially with the significant increase in the use Remote Condition Monitoring (RCM) systems on new trains. The cost of maintaining railway vehicles is a significant proportion of their whole life cost, up to 40%; further research is required to continue to improve the efficiency and reliability of rail vehicle maintenance.

The Special Issue seeks original research papers from researchers in industry or academia that focus on improving the efficiency, reliability and safety associated with rail vehicle maintenance. Papers describing a clear need for the work with a discussion on the route to implementation are of particular interest. Research topics that are of interest for this Special Issue are:

  • Use of robotics and automation for rail vehicle maintenance;
  • Developments in remote condition monitoring sensors and data processing
  • Predictive and prognostic maintenance;
  • Linking the outputs of remote condition monitoring systems to maintenance planning and depot management tools;
  • Use of machine learning and artificial intelligence to improve maintenance depot operations management.

Prof. Dr. Gareth Tucker
Prof. Dr. Simon Iwnicki
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. Machines is an international peer-reviewed open access monthly 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

  • rail vehicle
  • rolling stock
  • robotics
  • machine learning
  • RCM
  • prognostics
  • operations planning
  • knowledge engineering

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Published Papers (3 papers)

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Research

15 pages, 4249 KiB  
Article
Towards Robust and Effective Passive Compliance Design of End-Effectors for Robotic Train Fluid Servicing
by Kourosh Eshraghi, Mingfeng Wang and Cristinel Mares
Machines 2023, 11(11), 997; https://doi.org/10.3390/machines11110997 - 27 Oct 2023
Viewed by 1704
Abstract
Without mechanical compliance robots rely on controlled environments and precision equipment to avoid clashes and large contact forces when interacting with an external workpiece, e.g., a peg-in-hole (PiH) task. In such cases, passive compliance devices are used to reduce the insertion force (and [...] Read more.
Without mechanical compliance robots rely on controlled environments and precision equipment to avoid clashes and large contact forces when interacting with an external workpiece, e.g., a peg-in-hole (PiH) task. In such cases, passive compliance devices are used to reduce the insertion force (and in turn the robot payload) while guiding corrective motions. Previous studies in this field are limited to small misalignments and basic PiH geometries inapplicable to prevalent robotic and autonomous systems (RASs). In addition to these issues, our work argues that there is a lack of a unified approach to the development of passive compliance systems. To this end, we propose a higher-level design approach using robust engineering design (RED) methods. In a case study, we demonstrated this general approach with a Taguchi design framework, developing a remote centre compliant (RCC) end-effector for robotic train fluid servicing. For this specific problem, a pseudo-rigid-body model (PRBM) is suggested in order to save enormous computation time in design, modelling, and optimisation. Our results show that the compliant end-effector is capable of significantly reducing the insertion force for large misalignments up to 15 mm and 6 degrees. Full article
(This article belongs to the Special Issue Railway Vehicle Maintenance 4.0)
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19 pages, 509 KiB  
Article
Using Event Data to Build Predictive Engine Failure Models
by Pritesh Mistry, Peter Hughes, Abirami Gunasekaran, Gareth Tucker and Adam Bevan
Machines 2023, 11(7), 704; https://doi.org/10.3390/machines11070704 - 2 Jul 2023
Cited by 2 | Viewed by 2379
Abstract
Diesel engine failures are one reason for delays and breakdowns on the UK rail network, resulting in significant fines and related financial penalties for a train operating company. Preventing such failures is the ultimate goal, but forecasting or predicting future failures before they [...] Read more.
Diesel engine failures are one reason for delays and breakdowns on the UK rail network, resulting in significant fines and related financial penalties for a train operating company. Preventing such failures is the ultimate goal, but forecasting or predicting future failures before they occur would be highly desirable. In this study, we take real world Diesel Multiple Unit sensor data, recorded in the form of event data, and repurpose it for the remote condition monitoring of critical diesel engine operations. A methodology based on windowing of data is proposed that demonstrates the effective processing of event data for predictive modelling. This study specifically looks at predicting engine failures, and through this methodology, models trained on the processed data resulted in accuracies of 88%. Explainable AI methods are then utilised to provide feature importance explanations for the model’s performance. This information helps the end user understand specifically which sensor data from the larger dataset is most relevant for predicting engine failures. The work presented is useful to the railway industry, but more specifically to train operator companies who ideally want to foresee failures before they occur to avoid significant financial costs. The methodology proposed is applicable for the predictive maintenance of many systems, not just railway diesel engines. Full article
(This article belongs to the Special Issue Railway Vehicle Maintenance 4.0)
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11 pages, 3117 KiB  
Article
Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles Detection
by Marta Zamorano, María Jesús Gómez and Cristina Castejón
Machines 2023, 11(4), 493; https://doi.org/10.3390/machines11040493 - 19 Apr 2023
Cited by 5 | Viewed by 1585
Abstract
The detection of cracked railway axles by processing vibratory signals measured during operation is the focus of this study. The rotodynamic theory is applied to this specific purpose but, in practice and for real systems, there is no consensus on applying the results [...] Read more.
The detection of cracked railway axles by processing vibratory signals measured during operation is the focus of this study. The rotodynamic theory is applied to this specific purpose but, in practice and for real systems, there is no consensus on applying the results obtained from theory. Finding reliable patterns that change during operation would have advantages over other currently applied methods, such as non-destructive testing (NDT) techniques, because data between inspections would be obtained during operation. Vibratory signal processing techniques in the time-frequency domain, such as wavelet packet transform (WPT), have proved to be reliable to obtain patterns. The aim of this work is to develop a methodology to select the optimal function associated with the WPT, the mother wavelet (MW), and to find diagnostic patterns for cracked railway axle detection. In previous related works, the Daubechies 6 MW was commonly used for all speed/load conditions and defects. In this work, it was found that the Symlet 9 MW works better, so a comparative study was carried out with both functions, and it was observed that the success rates obtained with Daubechies 6 are improved when using Symlet 9. Specifically, defects above 16.6% of the shaft diameter were reliably detected, with no false alarms. To validate the proposed methodology, experimental vibratory signals of a healthy scaled railway axle were obtained and then the same axle was tested with a transverse crack located close to a section change (where this type of defect typically appears) for nine different crack depths. Full article
(This article belongs to the Special Issue Railway Vehicle Maintenance 4.0)
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