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dc.contributor.advisorMarais, H
dc.contributor.authorSchurz, Bernhard
dc.date.accessioned2024-01-29T12:58:06Z
dc.date.available2024-01-29T12:58:06Z
dc.date.issued2023-10
dc.identifier.urihttps://orcid.org/0000000286255244
dc.identifier.urihttp://hdl.handle.net/10394/42420
dc.descriptionMaster of Engineering in Computer and Electronic Engineering, North-West University, Potchefstroom Campusen_US
dc.description.abstractThis study aims to assist maintenance managers in identifying the need for predictive maintenance on a specific machine. The endeavour is to provide justification for condition monitoring implementation and outline the need for technological progress. Generally, industries are reluctant to change existing systems, and it takes feasibility studies and convincing data for the capital expense. Maintenance strategies are not set in stone and can vary from one facility to another and from one machine to another in a specific facility. The current maintenance management system can be modelled using plant data from a computerised maintenance management system (CMMS). With a low-budget intermediate condition monitoring technique, existing data or data from the literature, predictive maintenance strategies can be compared using the MatLab model created in the study. The model is used in a case study on a maize mill blower from which relevant conclusions are taken regarding the feasibility of a predictive maintenance strategy for the specific machine. The model can show a direct comparison of cost and time between the existing maintenance system and that of a condition-based maintenance system.en_US
dc.language.isoenen_US
dc.publisherNorth-West University (South Africa).en_US
dc.subjectMaintenance strategyen_US
dc.subjectPredictive maintenanceen_US
dc.subjectPreventative maintenanceen_US
dc.subjectCondition monitoringen_US
dc.subjectMaintenance modellingen_US
dc.subjectSimEventsen_US
dc.subjectStateFlowen_US
dc.titleQuantifying maintenance system efficiency of a lobe blower in a maize millen_US
dc.typeThesisen_US
dc.description.thesistypeMastersen_US
dc.contributor.researchID12806218 - Marais Henry-Jean (Supervisor)


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