Development of apps for predictive maintenance system a case study in hp
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Date
2018-05
Authors
Vemal Kathirvelu
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Abstract
In global manufacturing, manufactures from various nations aim to enhance their performance
by improving their manufacturing productivity among one another in order to maintain a
competitive advantage in this harsh business environment. Most of the manufactures have
implemented different kinds of manufacturing tools and methods such as Predictive
Maintenance (PdM) and Internet of Things (IoT) to make improvements in productivity.
Maintenance and support may account for as much as 60 to 75% of the total lifecycle cost of a
manufacturing system. Proper maintenance of manufacturing equipment is crucial to ensure
productivity and product quality. PdM forecasts failures in advance so that maintenance can be
better planned in order to save additional maintenance cost. IoT solutions in industrial
environments can lead nowadays to the development of innovative and efficient systems
aiming at increasing operational efficiency in a new generation of smart factories. In this paper,
a PdM method or system is developed to determine the most effective time to apply
maintenance to an equipment. This project presents a semantic framework for data collection,
synthesis, and knowledge sharing in a Cloud environment for PdM. The outcome is an Android
Application which informs users to perform maintenance at the right time.