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|Title:||Development of self - powered wireless sensing for electrical machine||Authors:||Sun, Yi||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2015||Abstract:||Machine condition monitoring technology is widely used among the industrial predictive maintenance for fault detection and diagnosis analysis via collecting operation parameters from machinery. Effective condition monitoring is significant to avoid catastrophic machine damages and breakdowns. The traditionally physically wired sensors have the problem of collecting data from certain machinery, such as rotating machines. To make matters worse, the maintenance costs, in terms of installation, mounting and battery replacements, take up major portions of the total budget. Under most circumstances, the maintenance expenses are higher than the sensors themselves. Therefore, wireless sensor that is flexible to be mounted on the hard-to-reach position in the machine is a promising solution and method for machine operation parameter measurements. The energy harvesting capability is added advantage for wireless sensor networks to remove the troublesome of battery maintenance. Hence, wireless sensing technology with energy harvesting capability is a potential and profitable solution for machine health condition monitor in industry in the near future. The purpose of this project is to develop wireless sensors with energy solutions to monitoring electrical machine health conditions. The final year project covers the primary stage of the entire project, which aims to construct data collection and record platform. In addition, the final year project also conducted experiment to test and monitor the performances of XBee modules, which were integrated with LM35 temperature sensors. LabVIEW was selected as the software development environment, on which the user interface of data collection and data logging is based. During the entire project, wireless communication technologies, wireless sensing technologies and energy harvesting technologies were massively reviewed and summarized in this report.||URI:||http://hdl.handle.net/10356/63828||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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