S. Manders, M. Pantelic, V. Milisavljevic, A. Martinetti
Self-engineering is a relatively new branch of knowledge that aims to understand how systems could “autonomously” re-configure or repair themselves without the intervention of the operators. A direct field of application is within the maintenance spectrum. Having systems or machines capable of self-detecting or even self-repairing could represent a game-changer, in capital asset fields such as the mining industry in particular. This paper aims to investigate the possible benefits and challenges of self-engineering / self-maintenance concerning mining machines, specifically bucket-wheel excavators (BWEs). Firstly, describing the state of the art and the main principles of self-engineering (and, particularly, the applications of self-maintenance) and the complexity of the mining industry in terms of machines and capital assets. Secondly, using as a real case example, the revitalization process of a 50,000 kg bucket-wheel excavator gearbox for an open-cast lignite mine in Serbia, pinpoints how self-engineering / self-maintenance could make the difference in managing the equipment. Finally, it discusses the results sketching the pros and cons of self-engineering in mining machines and similar capital assets.
{"title":"Self-Engineering: Possibilities for Maintenance Operations in the Mining Machines Industry","authors":"S. Manders, M. Pantelic, V. Milisavljevic, A. Martinetti","doi":"10.2139/ssrn.3945979","DOIUrl":"https://doi.org/10.2139/ssrn.3945979","url":null,"abstract":"Self-engineering is a relatively new branch of knowledge that aims to understand how systems could “autonomously” re-configure or repair themselves without the intervention of the operators. A direct field of application is within the maintenance spectrum. Having systems or machines capable of self-detecting or even self-repairing could represent a game-changer, in capital asset fields such as the mining industry in particular. This paper aims to investigate the possible benefits and challenges of self-engineering / self-maintenance concerning mining machines, specifically bucket-wheel excavators (BWEs). Firstly, describing the state of the art and the main principles of self-engineering (and, particularly, the applications of self-maintenance) and the complexity of the mining industry in terms of machines and capital assets. Secondly, using as a real case example, the revitalization process of a 50,000 kg bucket-wheel excavator gearbox for an open-cast lignite mine in Serbia, pinpoints how self-engineering / self-maintenance could make the difference in managing the equipment. Finally, it discusses the results sketching the pros and cons of self-engineering in mining machines and similar capital assets.","PeriodicalId":159245,"journal":{"name":"TESConf 2021: Design","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129676671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Rahimi, Haochen Liu, Miftahur Rahman, Cristóbal Ruiz Cárcel, I. Durazo-Cardenas, A. Starr, Amanda Hall, Robert Anderson
In the path towards the intelligent industrial 4.0, the railway industry is keen to develop intelligent asset management strategies for digitalization and smart management for rail infrastructure. It aims to both reduce the cost and exposure of human-labor, associated with track maintenance risk, as well as increase the autonomy and accuracy for the railway inspection and repair job. A Robotic Inspection and Repair System (RIRS) is proposed to undertake the automated railway maintenance consisting of the autonomous off-track travel between base workshop and track, road-rail conversion, autonomous on-track inspection, and repair as well as remote communicating to railway signaling system and infrastructure system. This paper presents a localization and navigation framework for this new autonomous system; applied to the mentioned railway maintenance job. This system comprises a commercial Unmanned Ground Vehicle (UGV, named Warthog) with a robotic manipulator (UR10e), and multiple onboard sensors including Lidar, camera, RTK GNSS, IMU, wheel odometry, and multiple types of cameras. An adaptive trolley is also designed for the purpose of road-rail conversion. This research also focuses on how to increase accuracy for the support of track defect detection and localization.
{"title":"Localisation and Navigation Framework for Autonomous Railway Robotic Inspection and Repair System","authors":"M. Rahimi, Haochen Liu, Miftahur Rahman, Cristóbal Ruiz Cárcel, I. Durazo-Cardenas, A. Starr, Amanda Hall, Robert Anderson","doi":"10.2139/ssrn.3945953","DOIUrl":"https://doi.org/10.2139/ssrn.3945953","url":null,"abstract":"In the path towards the intelligent industrial 4.0, the railway industry is keen to develop intelligent asset management strategies for digitalization and smart management for rail infrastructure. It aims to both reduce the cost and exposure of human-labor, associated with track maintenance risk, as well as increase the autonomy and accuracy for the railway inspection and repair job. A Robotic Inspection and Repair System (RIRS) is proposed to undertake the automated railway maintenance consisting of the autonomous off-track travel between base workshop and track, road-rail conversion, autonomous on-track inspection, and repair as well as remote communicating to railway signaling system and infrastructure system. This paper presents a localization and navigation framework for this new autonomous system; applied to the mentioned railway maintenance job. This system comprises a commercial Unmanned Ground Vehicle (UGV, named Warthog) with a robotic manipulator (UR10e), and multiple onboard sensors including Lidar, camera, RTK GNSS, IMU, wheel odometry, and multiple types of cameras. An adaptive trolley is also designed for the purpose of road-rail conversion. This research also focuses on how to increase accuracy for the support of track defect detection and localization.","PeriodicalId":159245,"journal":{"name":"TESConf 2021: Design","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130350817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yawar Abbas, A. Martinetti, M. Rajabalinejad, Lex Frunt, L. V. van Dongen
Learning from past experiences remains a key priority for industry and academia alike. Lessons learned are meant to facilitate this process of learning and are a well-known concept in project management, systems engineering, and knowledge management disciplines. However, given their embodied nature and presence in tacit and explicit forms, they are hard to acquire and fully realise through traditional means. This paper investigates lessons learned from system integration projects in the railway sector. System integration is an ongoing industrial challenge that is complex, multifaceted, hard to disentangle, and requires proper learning from prior experiences. By conducting an archival review and content analysis of thirty-seven documents (presented at an independent knowledge network), this paper investigates the frequency of ten key system integration patterns mentioned directly or indirectly at different interactive sessions (attended by a diverse group of stakeholders from multiple organisations). Consequently, the paper outlines five robust lessons learned to address these patterns. The underlying context of these lessons is also described to facilitate the development of appropriate approaches for their realisation. Finally, the paper discusses the implications of the presented lessons for policy making and systems performance management.
{"title":"Lessons Learned from System Integration: A Strategic Synopsis","authors":"Yawar Abbas, A. Martinetti, M. Rajabalinejad, Lex Frunt, L. V. van Dongen","doi":"10.2139/ssrn.3944608","DOIUrl":"https://doi.org/10.2139/ssrn.3944608","url":null,"abstract":"Learning from past experiences remains a key priority for industry and academia alike. Lessons learned are meant to facilitate this process of learning and are a well-known concept in project management, systems engineering, and knowledge management disciplines. However, given their embodied nature and presence in tacit and explicit forms, they are hard to acquire and fully realise through traditional means. This paper investigates lessons learned from system integration projects in the railway sector. System integration is an ongoing industrial challenge that is complex, multifaceted, hard to disentangle, and requires proper learning from prior experiences. By conducting an archival review and content analysis of thirty-seven documents (presented at an independent knowledge network), this paper investigates the frequency of ten key system integration patterns mentioned directly or indirectly at different interactive sessions (attended by a diverse group of stakeholders from multiple organisations). Consequently, the paper outlines five robust lessons learned to address these patterns. The underlying context of these lessons is also described to facilitate the development of appropriate approaches for their realisation. Finally, the paper discusses the implications of the presented lessons for policy making and systems performance management.","PeriodicalId":159245,"journal":{"name":"TESConf 2021: Design","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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