摩擦电效应
故障检测与隔离
断层(地质)
无线传感器网络
能量收集
振动
发射机
传感器节点
计算机科学
功率(物理)
工程类
电气工程
嵌入式系统
汽车工程
材料科学
实时计算
无线
无线传感器网络中的密钥分配
电信
无线网络
计算机网络
执行机构
地震学
复合材料
地质学
频道(广播)
物理
量子力学
作者
Wenjian Li,Yaoyao Liu,Shuwei Wang,Wei Li,Guoxu Liu,Junqing Zhao,Xiaohan Zhang,Chi Zhang
出处
期刊:IEEE-ASME Transactions on Mechatronics
[Institute of Electrical and Electronics Engineers]
日期:2020-05-08
卷期号:25 (5): 2188-2196
被引量:45
标识
DOI:10.1109/tmech.2020.2993336
摘要
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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