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Machines
Special Issues
System Health Condition Inspection, Monitoring, and Prognosis in Transportation
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System Health Condition Inspection, Monitoring, and Prognosis in Transportation

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

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 2316

Special Issue Editor

Dr. Moussa Hamadache

E-Mail Website
Guest Editor
Department of Electronic Electrical and Systems Engineering, Birmingham Center for Railway Research and Education (BCRRE), University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Interests: system health condition monitoring; fault detection and diagnosis; prognostics and health management; robotics (robots in manufacturing and robots in domicile); signal processing and filtration; control system and automation; mechatronics systems including applications to railway

Special Issue Information

Dear Colleagues,

At present, with the 4th Industrial Revolution and its associated rapid change in technology, industries, and societal patterns and processes (increased interconnectivity and smart automation), the efficient and safe operation of any advanced innovative machine should be a must. Transportation is an important and even crucial part of human life, and the monitoring and prognosis of urban infrastructure is safety-critical. Recent and future innovative system health condition inspection, monitoring, and prognosis technologies within transportation should be designed and tested toward their specific application(s) within their working conditions and environment. This could be through test rigs, test benches, demonstrators, and/or field implementations.    

This Special Issue titled “System Health Condition Inspection, Monitoring, and Prognosis in Transportation” welcomes original research and review articles on system health condition inspection, monitoring, and prognosis in transportation (railways, automotive, aviation, and/or maritime). Emphasis is placed on contributions with experimental applications (test rigs, test benches, demonstrators, and/or field implementations), both within higher-education organizations or within the industry—particularly the ones designed, built, and tested with promising results, and a strong emphasis on further development for real-world applications.

Dr. Moussa Hamadache
Guest Editor

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

  • machine health inspection
  • machine health condition monitoring
  • fault detection and diagnosis
  • prognostics and health management
  • transportation (railways, automotive, aviation, and maritime)
  • Industry 4.0 and IoT
  • experimental applications with AI technologies for transportation
  • machine learning (shallow and deep learning)
  • signal processing and filtration
  • sensors and actuators
  • signal and image processing methods
  • test rigs, test benches, demonstrators, and field implementations within transportation

Published Papers (1 paper)

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Research

21 pages, 14485 KiB  
Article
Experimental Fatigue Evaluation of Bogie Frames on Metro Trains
by Wei Zhou, Gangli Zhang, Hui Wang, Chang Peng, Xiang Liu, Heting Xiao and Xifeng Liang
Machines 2022, 10(11), 1003; https://doi.org/10.3390/machines10111003 - 31 Oct 2022
Cited by 3 | Viewed by 1908
Abstract
Metro vehicles have always been known for their high passenger density, frequent traffic flow and strong alternating loads due to their severe running environment. As the major support component, the bogie frame suffers from fatigue damages and receives a high intensity of interest. [...] Read more.
Metro vehicles have always been known for their high passenger density, frequent traffic flow and strong alternating loads due to their severe running environment. As the major support component, the bogie frame suffers from fatigue damages and receives a high intensity of interest. In this work, a theoretical model is presented between the measured strain and the structural stress via multiple load identification, wherein recognition matrices and stress evaluations of the bogie frame are defined according to the locations found in finite element analysis (FEA). The model is validated through random loading in FE simulation, and the load deviation is within 1.1 kN. A vehicle experiment was performed on the second bogie of the head car on a six-car metro train. The signed von Mises (SVM) stress was calculated at critical locations with the proposed method and compared with what was measured. The excessive part was no more than 14.97%, comparing the reconstructed with the measured amounts. Stress spectra were developed utilizing rain-flow counting and evaluated in terms of the damage accumulation rule with the optimal spectra groups determined from convergence analysis. The evaluation indicates that, when the running mileage increases to the full life cycle of 3,960,000 km, the maximum equivalent damage reaches 0.35 and 0.46 at the gear box base for measured and reconstructed amounts, respectively. Research outcomes suggest that the proposed method offers an alternative for fatigue assessment and maintenance strategies on metro vehicles, as well as other types of rail-transit vehicles. Full article
(This article belongs to the Special Issue System Health Condition Inspection, Monitoring, and Prognosis in Transportation)
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