Vibration sensors are involved extensively in a variety of applications. Especially in the era of the Internet of things, developing self-powered vibration sensors has become a very meaningful yet challenging problem. In this paper, a highly sensitive self-powered vibration sensor based on the TENG for machinery condition monitoring is investigated. The triboelectric layers constructed by the flexible dielectric film and porous metal material effectively improve the sensitivity of the TENG sensor. The TENG sensor can detect vibration of 1-2000 Hz, and the output signal of the TENG sensor has no distortion in waveforms even in high-temperature and high-humidity environments. Combined with machine learning algorithms, the TENG system has been successfully used to monitor the operating conditions of mechanical gear systems with high accuracy. The results can be displayed on both the computer screen and other mobile devices in real-time. Furthermore, it can be used for vibration detection in other areas such as the air compressor, heat gun, hollow tile recognition, etc. The detected data is further processed by an embedded system and displayed on the local screen. This work presents solid progress toward the practical applications of TENG in vibration detection and has great potential for the development of self-powered vibration sensing.
Tsinghua-Berkeley Shenzhen Institute (TBSI) is jointly established by Tsinghua University and University of California, Berkeley under the support of the Shenzhen Municipal Government. Especially, Data Science and Information Technology, full-English program, has dual degree for master program. Upon completion of degree requirements of TBSI and UC Berkeley, students may obtain a dual degree. As for PhD students, study will be completed in both Tsinghua University and UC Berkeley.
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