[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118149-en":3,"doc-seo-118149-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118149,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Leveraging digitilisation and machine learning for improved railway operations and maintenance - read online free","Reliable railway systems enable safe, efficient movement of goods and people, but quality assurance and maintenance remain costly and time-intensive. Rapid data growth from smart sensors and monitoring technologies enables machine learning to cut labour and expenses while improving reliability and safety through data-driven “Big Data” analytics. The paper shows how machine learning and data analysis support railway manufacturers and operators using rolling stock pantograph data, classifying events to identify faulty sensors, pantographs, or infrastructure and outlining implementation requirements.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia CIRP 120 (2023) 702–707  \n56th CIRP Conference on Manufacturing Systems, CIRP CMS‘23, South Africa  \nLeveraging digitilisation and machine learning for improved railway  \noperations and maintenance  \nM. Bezuidenhouta, J. L. Joostea *, D. Luckeb, C. J. Fouriea  \n*Department of Industrial Engineering, Stellenbosch University, 145 Banghoek Rd, Stellenbosch 7613, South Africa bESB Business School, Reutlingen University, Alteburgstraße 150, Reutlingen 72762, Germany  \n* Corresponding author. Tel.: +27218084234. E-mail [address: wyhan@sun.ac.za](address: wyhan@sun.ac.za)  \nAbstract  \nThe efficient and safe movement of goods and people require reliable railway systems. Quality assurance of manufactured and assembled systems and correct maintenance of such systems are required to keep rolling stock in good operational condition. Quality assurance and maintenance in the railway industry can be costly and time-consuming, but the expansive growth of data due to smart sensors and monitoring technologies makes it possible to leverage the potential of machine learning to reduce cost and labour. Improved reliability and safety, and reduced costs are benefits that the use of“Big Data” and machine learning techniques can realise. However, despite these potential benefits for manufacturers, rail operators, and passengers, the rail industry is still labelled for its lack of innovation, while in most other industries, data is regarded as a strategic asset for competitive advantage.  \nThis paper demonstrates how machine learning and data analysis can be used to benefit railway industry manufacturers and operators when applied to rolling stock data. It also illustrates the lost opportunity in the rail industry for not applying data-driven solutions to their full potential. The paper also discusses the current applications of machine learning in the railway industry and provides the requirements for the implementation of machine learning techniques. Machine learning is applied to pantograph data of a South African railway operator’s rolling stock. Classification – a machine learning technique – is used to identify and categorise events within the dataset to discover whether pantograph bounce occurs due to faulty sensors, faulty pantographs, or defective infrastructure. In this paper it is demonstrated how machine learning can benefit rail manufacturers and operators to improve manufacturing and assembly processes, as well as maintenance practices. It is concluded that railways should treat data similarly to other railway assets, with suitable management and governance practices.  \n© 2023 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023  \nKeywords: Machine Learning; Predictive Maintenance, Railway; Smart Sensors  \n1. Introduction  \nThe rise in digitalisation across various industry sectors and public entities provides the opportunity to create new value and increase competitiveness. In the railway sector this is especially true, with benefits including improved efficiency, lower operating costs and providing better service to customers [1] . Furthermore, rail companies and infrastructure managers can leverage digitalisation to enhance many segments of their  \nbusiness, such as manufacturing, operations and maintenance practices [1] .  \nAdvancements in predictive technology, data mining and machine learning have enhanced preventive and conditionbased maintenance practices to either supplement or replace previous generation maintenance systems, which often come with difficulties and inefficiencies [2] . The purpose of a maintenance programme is to ensu","cbCaip2Eq4kuZXCT","https://ap.wps.com/l/cbCaip2Eq4kuZXCT","pdf",524336,1,6,"English","en",105,"# Introduction\n## Digitalisation and competitiveness in rail\n## Predictive and condition-based maintenance\n## Data collection and condition monitoring\n## Big Data characteristics and machine learning rationale\n## Existing machine learning applications in other industries","[{\"question\":\"Why does the paper argue that machine learning can improve railway maintenance and operations?\",\"answer\":\"Smart sensors generate expanding datasets that traditional analysis struggles to use effectively. Machine learning can turn this data into actionable insights for reliability, safety, and lower maintenance cost and labour.\"},{\"question\":\"What dataset and technique are used in the study?\",\"answer\":\"Machine learning is applied to pantograph data from a South African railway operator. Classification is used to identify and categorise events to determine whether issues originate from faulty sensors, faulty pantographs, or defective infrastructure.\"},{\"question\":\"What implementation requirements does the paper discuss?\",\"answer\":\"The paper discusses requirements for implementing machine learning techniques, including the need to collect appropriate data and transform monitored signals into valuable information for decision-making and maintenance planning.\"}]","Leveraging digitilisation and machine learning for improved railway operations and maintenance - read online free | PDF",1785681889,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"leveraging-digitilisation-and-machine-learning-for-improved-railway-operations-and-maintenance-read-online-free","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/leveraging-digitilisation-and-machine-learning-for-improved-railway-operations-and-maintenance-read-online-free/118149/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does the paper argue that machine learning can improve railway maintenance and operations?","Question",{"text":75,"@type":76},"Smart sensors generate expanding datasets that traditional analysis struggles to use effectively. Machine learning can turn this data into actionable insights for reliability, safety, and lower maintenance cost and labour.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and technique are used in the study?",{"text":80,"@type":76},"Machine learning is applied to pantograph data from a South African railway operator. Classification is used to identify and categorise events to determine whether issues originate from faulty sensors, faulty pantographs, or defective infrastructure.",{"name":82,"@type":73,"acceptedAnswer":83},"What implementation requirements does the paper discuss?",{"text":84,"@type":76},"The paper discusses requirements for implementing machine learning techniques, including the need to collect appropriate data and transform monitored signals into valuable information for decision-making and maintenance planning.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]