Journal
IEEE-ASME TRANSACTIONS ON MECHATRONICS
Volume 25, Issue 5, Pages 2188-2196Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2020.2993336
Keywords
Vibrations; Fault detection; Resonant frequency; Copper; Electrodes; Sensors; Power system management; Internet of Things; machine fault detection; self-powered system; triboelectric nanogenerator (TENG); vibration energy harvesting
Categories
Funding
- Natural Science Foundation of China [51922023, 61874011]
- Beijing Natural Science Foundation [4192070]
- National Key Research and Development Program of China [2016YFA0202704]
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Physical parameter sensing largely benefits the lifetime and operational costs of machines and has been widely used for machine fault detection. Herein, in this article, we developed a multinode sensor network, which is fully self-powered by harvesting mechanical vibration energy, to establish a machine fault detection system. A multilayered vibrational triboelectric nanogenerator (V-TENG) was designed to scavenge energy from working machines. Triggered by a vibration motion with the frequency of 8 Hz, the V-TENG can generate an output with power density of 3.33 mW/m(3). With a power management module, the microcontrol unit integrated with sensors and a wireless transmitter can be continuously powered by the V-TENG to construct a self-powered vibration sensor node (SVSN). A supporting vector machine algorithm-based machine fault detection system was then established through a three-SVSN network by acquiring acceleration and temperature data from the working machine. Based on the system, different working conditions of the machine were recognized with an accuracy of 83.6%. The TENG-based SVSN for machine fault detection has demonstrated wide prospects in production monitoring, intelligent manufacturing, and smart factory. Moreover, the proposed self-powered sensor network has great potential and wide application in the era of distributed Internet of Things, artificial intelligence, and big data.
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