Development of apps for predictive maintenance system a case study in hp
| dc.contributor.author | Vemal Kathirvelu | |
| dc.date.accessioned | 2021-05-17T09:09:41Z | |
| dc.date.available | 2021-05-17T09:09:41Z | |
| dc.date.issued | 2018-05 | |
| dc.description.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/13435 | |
| dc.language.iso | en | en_US |
| dc.title | Development of apps for predictive maintenance system a case study in hp | en_US |
| dc.type | Other | en_US |
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