汇报题目：参加SPIE International Symposium on Smart Structures/NDE 2018 参会报告

汇报时间：2018年3月13日(星期二) 19:00

汇报地点：科技园西五楼南205会议室

汇报人：张颖

会议名称：SPIE International Symposium on Smart Structures/NDE 2018

会议时间：4-8 March 2018

会议地点：Denver, Colorado, the USA.

会议简介：SPIE Smart Structures + Nondestructive Evaluation showcases the latest research in advanced materials, electroactive polymers (EAP), bioinspiration and biomimetics for robotics design, along with energy harvesting, sensor networks, nondestructive evaluation (NDE) and structural health monitoring (SHM) for automotive, aerospace, civil infrastructure, smart factories, and Industry 4.0.

会议交流工作

Oral presentation: A Rotational Energy Harvester for Wireless Health Condition Monitoring by Utilizing Intrinsic Structure of Bearing

报告人：张颖

参加论文信息

Title: A Rotational Energy Harvester for Wireless Health Condition Monitoring by Utilizing Intrinsic Structure of Bearing

Author: Ying Zhang, Junyi Cao, Jing Lin

Abstract: Nowadays, the energy harvesting has attracted considerable interest of international researchers from many disciplines, due to the promising application around the industrial area. The energy harvesting technology is very important because it can extract electrical energy from ambient environment for supplying power to wireless monitoring nodes. Although much work has been done for extracting energy from vibration motions, there are few successful applications for the rotational motions. Also, those energy harvesters for rotational motion will be incapable for realistic applications when the priori knowledge of monitoring objectives is absent. This paper proposes a novel electromagnetic energy harvester for the online bearing health condition monitoring. The circular Halbach array is introduced to the arrangement of permanent magnets, which can achieve frequency-up conversion and enhance the magnetic field. In order to optimize output power performance, the theoretical magnetic field model to analyze the magnetic coupling is established. The proposed dynamic model can predict voltage response and output power. The numerical and experimental results show that the output power and power density can be improved for condition monitoring.