汇报安排

题目：参加WOM2017国际会议总结报告会

时间：2017年4月5日（星期三） 20：30-21:10

地点：我院轴承所会议室（中三楼1330）

报告人：博1401 彭业萍

会议安排

会议名称：21st International Conference on Wear of Materials (WOM) 2017

会议时间：26 - 30 March 2017

会议地点：California, USA

会议简介：

The 21st International Conference on Wear of Materials focuses on both the fundamental and applied aspects of wear and friction of materials at the macro-, micro-, and nano-scale. It will address the understanding of tribological phenomena; particularly the progression in recent decades. Special sessions will concentrate on wear of tools and tooling materials, hot and cold erosion (droplet, solid and cavity-based erosion), wear of brake/frictional materials, marine wear systems, role of third bodies during wear, and surface texturing for wear reduction.

In addition, the conference provides a unique international forum for researchers and practicing engineers from different disciplines to interact and exchange their latest understandings.

会议交流工作

Presentation: Wear state identification using dynamic features of wear debris for online purpose. 张贴报告人：彭业萍

参加论文信息

Title：Wear state identification using dynamic features of wear debris for online purpose

Author：Yeping Peng, Tonghai Wu, Shuo Wang, Zhongxiao Peng

Abstract: Wear status identification including wear rate estimation and wear mechanism assessment can be performed using wear debris information. However, although on-line monitoring methods have distinctive advantages over off-line approaches, existing on-line monitoring methods provide limited features of wear particles and have difficulties characterising complex wear states. Most of them determine wear status based on changes in the wear rates, and the wear mechanisms are not taken into consideration. Therefore, comprehensive wear state identification is a bottleneck in real-time machine health monitoring for condition-based maintenance. In order to further advance on-line monitoring technology, this paper, in a case study format, presents a new approach for wear state characterisation using comprehensive wear debris features. For this purpose, wear experiments were carried out on a four-ball rig, and a particle imaging system was employed to capture videos of moving particles to acquire dynamic features. Based on this, wear particles were firstly counted to characterise wear rate. In this stage, a statistical clustering model was established using a mean-shift algorithm to categorise wear debris samples. A trend of wear state evolution was thus obtained. Secondly, the size, shape and colour of wear debris were extracted to identify particles into fatigue, sliding and oxides for wear mechanism analysis. The analysis results of wear mechanisms were related to the trend of the wear state. Correspondingly, a changing chart that contains the wear degree and wear mechanisms was drawn. Therefore, an on-line system has been developed to capture comprehensive particle information to assess the wear severity and mechanisms for in-depth wear analysis and full-life machine condition monitoring.

欢迎各位感兴趣的同学届时前来交流。