|dc.contributor.author||Akindele, Babatunde Babajide||en_US
|dc.description||Thesis (M.Ing. (Development and Management))--North-West University, Potchefstroom Campus, 2011.||
|dc.description.abstract||Predictive maintenance is a proactive maintenance strategy that is aimed at preventing the unexpected failure of equipment through condition monitoring of the health and performance of the equipment.
Incessant equipment outage resulting in low availability of production facilities is a major issue in the Nigerian manufacturing environment. Improving equipment availability in Nigeria industry through institution of a full featured predictive maintenance has been suggested by many authors. The key to instituting a full-featured predictive maintenance is condition monitoring.
Primarily, this research is focused on how to reduce the prevalent of equipment downtime in the Nigerian manufacturing industry, through the application of Model Based Fault Diagnosis technology as a condition monitoring tool for enhancing predictive maintenance practices in Nigerian manufacturing industry.
The following objectives underscore the aim of this research work:
* To assess the implementation and performance of predictive maintenance practices in some selected manufacturing companies in Nigeria and verify if there is need for improvement in these practices.
* To identify the challenges and barriers to the implementation and performance of full-featured predictive maintenance practice in the Nigerian manufacturing industry.
* To develop a framework for enhancing quality of Predictive Maintenance practices in the manufacturing industry in Nigeria through a Model Based Fault Diagnosis and Decision Support System.
* To validate that the developed framework meets the Nigerian manufacturing industry needs through the implementation of a prototype in one of the selected manufacturing companies in the case study.
The empirical investigation undertaken as part of this research revolves around five (5) of the Nigerian manufacturing companies. Personal interviews were also adopted as means of data collection.
The research outcomes reveal the followings:
* Top management commitment to the implementation of predictive maintenance strategies in the Nigerian manufacturing industry is inadequate.
* Many of the manufacturing companies lack a tool for carrying out continuous condition monitoring in their predictive maintenance program. This is responsible for poor performance of most predictive maintenance programs in Nigerian manufacturing industry.
* Inadequate training on the implementation of predictive maintenance principles is adversely affecting the proficiency of personnel in adopting philosophy that underlies practices of predictive maintenance.
* The size of equipment part inventory, maintenance work backlog and machine scraps are also enormous in the maintenance yards of the companies.
* Nevertheless, the implementation of predictive maintenance program has a positive impact on the equipment availability of one of the case studies. Management commitment in Chemical and Allied Products (CAP) Plc is outstanding. Application of intelligent condition monitoring system, and personnel training and competence are vital to the success of Predictive Maintenance implementation in CAP Plc.
The specific deliverable from this research is a proposed framework (MBFDF) for effective implementation of predictive maintenance strategy through application of model based fault diagnosis technology, which can be adopted to improve performance of predictive maintenance practices in the Nigerian manufacturing industry.
The deliverable also includes a soft copy of data in Excel spreadsheet obtained during experimental test of the proposed framework in a small manufacturing company in Nigeria.
In this research, a model based fault diagnosis framework (MBFDF) to serve as a condition monitoring and decision support tool for predictive maintenance programs in Nigerian manufacturing industry was developed. Implementation to verify the real-life implementability and effectiveness of the proposed framework was performed in one of the companies used for the case study.
A comparison of results with pre-integration predictive maintenance program is presented, showing the implementability and the effectiveness of the proposed MBFDF for condition monitoring in predictive maintenance programs in the Nigerian manufacturing company.
Recommendations presented in this dissertation are also vital to the success of implementing predictive maintenance program in Nigerian manufacturing companies.||en_US
|dc.title||Model-based fault diagnosis framework for effective predictive maintenance||en