Today’s modern maanufacturing demand that production systems be monitored continuously, in real time, to ensure reliability, safety of manufacturing processes and quality of products. The integration of intelligent sensors into production systems enables very specific tasks to be performed, such as remote monitoring and communicating quickly the relevant information concerning deterioration detected on these systems. This is of major interest to guide mantenance manager to make decision regarding maintenance actions.The objective of this article is to investigate the impact of maintenance policies on the performance of manufacturing systems. The integration of intelligent sensors networks is proposed for monitoring equipments to implement predictive maintenance policy. We develop simulation studies to investigate the impact of the intelligent sensor on predictive maintenance cost and reliability. The optimum predictive maintenance time can be found using the threshold of the reliability data issue from the sensors. The methods are based on the cost per unit time to show how different parameter impact the cost of maintenance, To perform this study a numerical example is illustrate the model.

Impact of intelligent wireless sensor network on predictive maintenance cost

Faccio M.;
2018

Abstract

Today’s modern maanufacturing demand that production systems be monitored continuously, in real time, to ensure reliability, safety of manufacturing processes and quality of products. The integration of intelligent sensors into production systems enables very specific tasks to be performed, such as remote monitoring and communicating quickly the relevant information concerning deterioration detected on these systems. This is of major interest to guide mantenance manager to make decision regarding maintenance actions.The objective of this article is to investigate the impact of maintenance policies on the performance of manufacturing systems. The integration of intelligent sensors networks is proposed for monitoring equipments to implement predictive maintenance policy. We develop simulation studies to investigate the impact of the intelligent sensor on predictive maintenance cost and reliability. The optimum predictive maintenance time can be found using the threshold of the reliability data issue from the sensors. The methods are based on the cost per unit time to show how different parameter impact the cost of maintenance, To perform this study a numerical example is illustrate the model.
Proceedings of International Conference on Optimization and Applications
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3290850
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