Vibration sensors are widely used in the fields of infrastructure health, vehicle safety, biomedical equipment, intelligent electronic products, and mechanical equipment vibration monitoring. However, new challenges have shown up with the booming development of the Internet of things and sensor networks, especially in scenarios where extensive sensing/monitoring is required yet in-situ power supply is not easily accessible. One appealing alternative to this problem is to develop self-powered sensors, of which self-powered vibration sensing is an important subject.
At the same time, with the development of the IoT, machinery and equipment are evolving towards more automation, higher intelligence, and better efficiency. For example, unmanned ships can undertake large-scale, long-term, low-cost missions, with great implications in both commercial and security sectors. However, mechanical equipment is expected to operate stably for a long time, and its operating condition should be monitored in real-time. Once the operating state becomes abnormal, measures need to be taken immediately. The equipment’s sensors may be frequently exposed to harsh environments with high temperatures and high humidity. Furthermore, considering the cost and power consumption of the sensors in a broader sense, the sensor needs to work normally even in a power system failure. In this sense, self-powered vibration sensors have unique advantages.
Recently, the Smart Sensing and Robotics (SSR) research group led by Assistant Professor Wenbo DING from Tsinghua-Berkeley Shenzhen Institute (TBSI) has developed a highly sensitive self-powered vibration sensor based on the triboelectric nanogenerator (TENG). 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 (amplitude may change) 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.
Figure 2. Structure and working principle of the TENG
Figure 3. Machinery condition monitoring with the TENG system
The research article entitled “A Highly Sensitive Triboelectric Vibration Sensor for Machinery Condition Monitoring” was recently published in the journal Advanced Energy Materials and selected as the front cover paper of the current issue. The corresponding authors are Assistant Professor Wenbo DING and Dr. Jiyu WANG (TBSI). The co-first authors are Hongfa ZHAO, Zihao AI (TBSI) and Mingrui SHU (Tsinghua SIGS). Authors also include Dr. Zirui LOU, Kit-Wa SOU, Chengyue LU, Yuchao JIN, Zihan WANG from TBSI, Assistant Professor Yidan CAO and Associate Professor Xiaomin XU from Tsinghua SIGS, and Assistant Professor Changsheng WU from the National University of Singapore. This work is supported by the National Natural Science Foundation of China; Guangdong Basic and Applied Basic Research Foundation; Shenzhen Stable Supporting Program; the Institute for Guo Qiang, Tsinghua University; and Tsinghua Shenzhen International Graduate School.
Link to full article:
Research News | New highly sensitive triboelectric vibration sensor developed
Written by Hongfa ZHAO
Images by Dr. Wenbo Ding’s research group
Edited by Alena Shish & Yuan Yang