Journal article 378 views 179 downloads
Condition-based maintenance for major airport baggage systems
Journal of Manufacturing Technology Management, Volume: 32, Issue: 3, Pages: 722 - 741
Swansea University Author: Nicholas Rich
PDF | Accepted ManuscriptDownload (694.04KB)
DOI (Published version): 10.1108/jmtm-04-2019-0144
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...
|Published in:||Journal of Manufacturing Technology Management|
Check full text
No Tags, Be the first to tag this record!
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
Industry 4.0, Maintenance, Technological Innovation
Faculty of Humanities and Social Sciences