View Record

TitleIntelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems
AuthorWeppenaar, De Ville
SubjectCentral University of Technology, Free State - Dissertations
SubjectPlant maintenance - Management
SubjectIndustrial equipment - Maintenance and repair
SubjectMultiagent systems
SubjectIntelligent agents (Computer software)
SubjectElectronic apparatus and appliances - Maintenance and repair - Computer- assisted instruction
SubjectDissertations, academic - South Africa - Bloemfontein
Format4 563 110 bytes
AbstractThesis (M. Tech.) -- Central University of Technology, Free State, 2010
AbstractTraditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation.
PublisherBloemfontein : Central University of Technology, Free State