Collective Approach for Repair time Analysis
Machine downtime can be defined as a total amount of time the machine would normally be out of service from the moment it fails until the moment it is fully repaired and back to operate. Once a unit experiences a service downtime or downgrade, the covariates or risk factors can directly impact...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en en |
| Published: |
IEEE
2006
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/11451/1/INDIN06.pdf http://eprints.utem.edu.my/id/eprint/11451/2/Burhanuddin%282006%29%2C_INDIN06%2C_Collective_Approach_for_Repair_time_Analysis.pdf http://eprints.utem.edu.my/id/eprint/11451/ |
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| Summary: | Machine downtime can be defined as a total amount
of time the machine would normally be out of service from the
moment it fails until the moment it is fully repaired and back to
operate. Once a unit experiences a service downtime or
downgrade, the covariates or risk factors can directly impact
on the delay in repairing activities. Our study reveals the model
to identify the 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 the facilitators.
There are not many 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. The proposed study extended Choy, John, Thomas & Yan [1] models using either semi-parametric or non-parametric approaches of reliability analysis to examine the relationship between repair time and various risk factors of interest. Then the models will be embedded to neural networks to provide better estimation of repairing parameters. The proposed models can be used by maintenance managers as a benchmarking to develope quality service to enhance competitiveness among service providers in corrective maintenance field. Also the models can be deployedfarther to develop a computerized decision support system. |
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