学术动态
学术动态
博士生董晓妮出国参加国际会议回国汇报通知
汇报题目:参加The 7th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017) 参会报告
汇报时间:2017年9月11日(星期一) 19:30
汇报地点:兴庆校区西二楼东121会议室
汇报人:董晓妮
会议名称:The First World Congress on Condition Monitoring (WCCM 2017)
会议时间: 31 July – 4 August, 2017
会议地点:Sheraton Princess Kaiulani, Hawaii, USA.
会议简介:The International Society for Condition Monitoring (ISCM) and the British Institute of Non-Destructive Testing (BINDT) are delighted to invite you to this global event. This congress is of major significance, being the first world event in its field. Ownership and overall control of the event rests with the ISCM and this premier event is being organised in the United Kingdom by BINDT in collaboration with almost all condition monitoring and NDT societies worldwide. This combination of efforts will create one of the largest events of its kind at a truly international level. The event will provide you with a unique opportunity to network with leading academics and industrialists from all over the world in the field of condition monitoring and related areas.
会议交流工作
Oral presentation: Frequency Selection for On-line Identification of Welding Penetration through Audible Sound
报告人:董晓妮
参加论文信息
Title: Frequency Selection for On-line Identification of Welding Penetration through Audible Sound
Author: Xiaoni Dong, Wenjing Ren, Riwei Luan, Zhe Yang and Guangrui Wen,Zhifen Zhang
Abstract: Audible sound sensing technology is a possible key to real-time monitoring and controlling of welding quality and process for intelligent manufacturing of robotic welding. In this paper, the selection of frequency components from audible sound signal was carefully researched for identifying three types of seam penetration, e.g., under penetration, normal penetration and burning through by means of Principal Component Analysis (PCA). The selected principal components were carefully analyzed. Then, the ability of detecting and identifying different weld defects was thoroughly discussed and demonstrated. At the end, the degree of data redundancy and noise were quantitatively evaluated and discussed. In this paper, PCA has been verified to be able to effectively reduce the feature dimension and accurately identify the different weld defects using the selected feature in real-time for robotic welding.

 

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