Integrating simulation modeling and equipment condition diagnostics for predictive maintenance strategies-a case study
Luis Rene Contreras, C. Modi, Arunkumar Pennathur
Abstract
Luis Rene Contreras, C. Modi, Arunkumar Pennathur
Abstract
This paper presents results from a case study in predictive maintenance at a distribution warehouse. A simulation model was built with ARENA/spl trade/ 5.0 for integrating predictive maintenance strategies with production planning strategies, for a conveyor system. Equipment health was monitored using condition-based parameters such as temperature and vibration for mechanical and electrical components such as rollers, electrical motors, and gearboxes. This diagnostic information was then integrated with a simulation model to simulate various equipment breakdown and failure conditions. Integration of condition-based monitoring of conveying equipment with a simulation model of the distribution system has provided a useful analytical tool for management to reduce production downtime due to unplanned maintenance activities - in this instance, downtime was reduced by more than 50% and work in process inventory was reduced by more than 65%.
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This paper presents results from a case study in predictive maintenance at a distribution warehouse. A simulation model was built with ARENA/spl trade/ 5.0 for integrating predictive maintenance strategies with production planning strategies, for a conveyor system. Equipment health was monitored using condition-based parameters such as temperature and vibration for mechanical and electrical components such as rollers, electrical motors, and gearboxes. This diagnostic information was then integrated with a simulation model to simulate various equipment breakdown and failure conditions. Integration of condition-based monitoring of conveying equipment with a simulation model of the distribution system has provided a useful analytical tool for management to reduce production downtime due to unplanned maintenance activities - in this instance, downtime was reduced by more than 50% and work in process inventory was reduced by more than 65%.
Key concepts: Downtime, Predictive maintenance, Reliability engineering, Condition monitoring, Condition-based maintenance, Prognostics, Process (computing), Engineering