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A wireless motor-condition, precise analysis system using a highly efficient vibration-energy harvester
A wireless motor condition precise analysis system that uses a highly efficient energy harvester is described in this paper. A continuation data sampling is required for the condition analysis of a motor. We developed the technology that makes a vibration energy harvester highly efficient and reduce...
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Main Authors: | , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | A wireless motor condition precise analysis system that uses a highly efficient energy harvester is described in this paper. A continuation data sampling is required for the condition analysis of a motor. We developed the technology that makes a vibration energy harvester highly efficient and reduces the energy consumption of the whole system. A battery-less wireless analysis system was created in which the operation and life prediction of a motor was made possible. The original structure of the highly efficient energy harvester was developed using magnetic strain material rods. Electric power is generated using the coils around the rods when the rods are bent by external vibration. Therefore, the efficiency of the energy harvester is raised by optimizing the internal stress of the rods using buffer plates. The energy harvester has a resonant frequency of 90 Hz, a maximum electromotive force of 2.7 V, and a maximum power of 28 mW using 0.5-G acceleration. The wireless motor condition monitoring system includes the energy harvester, an end device, a coordinator, and a PC for data processing. The end device for the monitoring and data transfer is operated using only the electric power from the energy harvester. The monitoring information on the temperature, humidity, supplied voltage, and communication quality every three seconds and for the vibration waveform every five seconds is transmitted to the coordinators by the end device and handled by the PC. This enables for the condition monitoring and fault prediction of the motor. |
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ISSN: | 1935-4576 2378-363X |
DOI: | 10.1109/INDIN.2013.6622918 |