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Condition-based maintenance for major airport baggage systems

Frank Koenig, Pauline Anne Found, Maneesh Kumar, Nicholas Rich Orcid Logo

Journal of Manufacturing Technology Management, Volume: 32, Issue: 3, Pages: 722 - 741

Swansea University Author: Nicholas Rich Orcid Logo

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DOI (Published version): 10.1108/jmtm-04-2019-0144

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Purpose: The aim of this paper is to develop a contribution to knowledge that adds to theempirical evidence of predictive condition-based maintenance by demonstrating how theavailability and reliability of current assets can be improved without costly capital investment,resulting in overall system p...

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Published in: Journal of Manufacturing Technology Management
ISSN: 1741-038X
Published: London Emerald 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa54583
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first_indexed 2020-07-01T13:44:31Z
last_indexed 2021-04-01T03:19:37Z
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spelling 2021-03-31T11:11:52.9062841 v2 54583 2020-07-01 Condition-based maintenance for major airport baggage systems 272a3165694c25efa85725e514ebbcd3 0000-0003-0216-2807 Nicholas Rich Nicholas Rich true false 2020-07-01 BBU Purpose: The aim of this paper is to develop a contribution to knowledge that adds to theempirical evidence of predictive condition-based maintenance by demonstrating how theavailability and reliability of current assets can be improved without costly capital investment,resulting in overall system performance improvements.Methodology: The empirical, experimental approach, technical action research (TAR), wasdesigned to study a major Middle-Eastern airport baggage handling operation. A predictivecondition-based maintenance prototype station was installed to monitor the condition of ahighly complex system of static and moving assets.Findings. The research provides evidence that the performance frontier for airport baggagehandling systems can be improved using automated dynamic monitoring of the vibration anddigital image data on baggage trays as they pass a service station. The introduction of low-endinnovation, which combines advanced technology and low-cost hardware, reduced assetfailures in this complex, high speed operating environment.Originality/Value: The originality derives from the application of existing hardware with thecombination of Edge and Cloud computing software through architectural innovation resultingin adaptations to an existing baggage handling system within the context of a time-criticallogistics system.Keywords: IoT, Condition-based maintenance, Predictive maintenance, Edge computing, IoT,Technical Action Research, Theory of Performance Frontiers,Case Study Journal Article Journal of Manufacturing Technology Management 32 3 722 741 Emerald London 1741-038X Industry 4.0, Maintenance, Technological Innovation 17 7 2020 2020-07-17 10.1108/jmtm-04-2019-0144 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2021-03-31T11:11:52.9062841 2020-07-01T14:28:51.2104120 Faculty of Humanities and Social Sciences School of Management - Business Management Frank Koenig 1 Pauline Anne Found 2 Maneesh Kumar 3 Nicholas Rich 0000-0003-0216-2807 4 54583__18003__02bf7fb4a01c44febb9e95689d9fdf59.pdf AAM.pdf 2020-08-20T18:31:57.4798745 Output 710698 application/pdf Accepted Manuscript true true eng
title Condition-based maintenance for major airport baggage systems
spellingShingle Condition-based maintenance for major airport baggage systems
Nicholas Rich
title_short Condition-based maintenance for major airport baggage systems
title_full Condition-based maintenance for major airport baggage systems
title_fullStr Condition-based maintenance for major airport baggage systems
title_full_unstemmed Condition-based maintenance for major airport baggage systems
title_sort Condition-based maintenance for major airport baggage systems
author_id_str_mv 272a3165694c25efa85725e514ebbcd3
author_id_fullname_str_mv 272a3165694c25efa85725e514ebbcd3_***_Nicholas Rich
author Nicholas Rich
author2 Frank Koenig
Pauline Anne Found
Maneesh Kumar
Nicholas Rich
format Journal article
container_title Journal of Manufacturing Technology Management
container_volume 32
container_issue 3
container_start_page 722
publishDate 2020
institution Swansea University
issn 1741-038X
doi_str_mv 10.1108/jmtm-04-2019-0144
publisher Emerald
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description Purpose: The aim of this paper is to develop a contribution to knowledge that adds to theempirical evidence of predictive condition-based maintenance by demonstrating how theavailability and reliability of current assets can be improved without costly capital investment,resulting in overall system performance improvements.Methodology: The empirical, experimental approach, technical action research (TAR), wasdesigned to study a major Middle-Eastern airport baggage handling operation. A predictivecondition-based maintenance prototype station was installed to monitor the condition of ahighly complex system of static and moving assets.Findings. The research provides evidence that the performance frontier for airport baggagehandling systems can be improved using automated dynamic monitoring of the vibration anddigital image data on baggage trays as they pass a service station. The introduction of low-endinnovation, which combines advanced technology and low-cost hardware, reduced assetfailures in this complex, high speed operating environment.Originality/Value: The originality derives from the application of existing hardware with thecombination of Edge and Cloud computing software through architectural innovation resultingin adaptations to an existing baggage handling system within the context of a time-criticallogistics system.Keywords: IoT, Condition-based maintenance, Predictive maintenance, Edge computing, IoT,Technical Action Research, Theory of Performance Frontiers,Case Study
published_date 2020-07-17T04:08:13Z
_version_ 1763753583506882560
score 10.999161

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