Modeling and Analysis of Repairable System for Quality Corrective Maintenance Management Using Neural Network - M.A., Burhanuddin; A.R., Ahmad; H., Habibollah (UTM, 2006)
Breakdown or failure can be defined as total amount of time the equipment would normally be out of service from the moment it fails until the moment it is fully repaired and operational. Once a unit experiences a service downtime or downgrade, the covariates or risk factors can directly impact on the delay in repairing activities. This study reveals the model to identify potential risk factors that either delay or accelerate repair times, and it also demonstrates the extent of such delay, attributable to specific risk factors. Once risk factors are detected, the maintenance planners and maintenance supervisors are aware of the starting and finishing points for each repairing job due to their prior knowledge about the potential barriers and facilitators. There are also not sufficient studies made on the application of artificial intelligence techniques to access troubleshooting activities as it always taken into consideration in a verbal sense and yet is not dealt with mathematically. Therefore, the proposed study employs either parametric and non-parametric approaches of reliability analysis to examine the relationship between repair time and various risk factors of interest. To achieve this, non-parametric and semi-parametric approaches will be embedded to artificial intelligence to provide better estimation of repairing parameters. We hope that the proposed model can be used by maintenance managers as a benchmarking to develope quality service to enhance competitiveness among service providers in corrective maintenance field.