1. 国际会议信息

·会议名称： IEEE I²MTC 2016(2016 IEEE International Instrumentation and Measurement Technology Conference)

·会议时间： 23 – 26 MAY 2016

·会议地点： 台北, 台湾

·会议简介：国际仪表与测量技术会议（International Instrumentation and Measurement Technology Conference，I²MTC）是IEEE仪表与测量学会（IEEE Instrumentation and Measurement Society）主办的仪器仪表领域顶级国际学术会议，会议主题包括：仪表与测量基础、传感器与换能器、物理量测量、测量系统、测量应用、信号与图像处理、监测与故障诊断和工业应用等。其分会SPECIAL SESSION: Advances in instruments and measurement, Advanced Measurement and Instrumentation专场讨论机械系统的健康监测与故障诊断，涉及图像处理技术、电机故障诊断分类、深度学习等。

·会议交流工作：

Presentation –Fault diagnosis from visualization perspective using stream statistics. (杨昂)

Presentation –Improved VMD for Fault Feature Visualization to Identify Wheel Set Bearing Fault of High Speed Locomotive. (李紫鹏)

2. 回校汇报申请信息

·申请汇报时间：2016年6月24日 晚上7点

·申请汇报地点：曲江科技园机械制造系统工程国家重点实验室 A228室

·汇报申请人：杨昂，李紫鹏

3. 参会论文信息

·论文标题： Fault diagnosis from visualization perspective using stream statistics

·作者： 杨昂，王宇，訾艳阳，陈景龙，潘骏

·摘要：This paper proposed a concept called “stream statistics” for fault diagnosis. Its idea is to count the obtained signal’s distribution in all intervals and transform them to statistical features. This idea differs from the conventional time and frequency domain methods and offers promising advantages, e.g., no need to select parameters and less calculation, over the conventional ones. To cope with the accompanied high dimensional problem, we apply the linear discriminant analysis (LDA) method for projecting statistical features to 2D or 3D space, which is feasible for visualization with the purpose of fault diagnosis. Other dimensionality reduction method, principle component analysis (PCA), is took into comparison in order for demonstrating the advantages of the proposed method. The visualization results of motor bearing data & hard disk drive (HDD) data prove the effectiveness of the proposed method. Moreover, the relationship between visualization results and condition monitoring is established and a modification for LDA based on original criterion function is given.

·论文标题： Improved VMD for Fault Feature Visualization to Identify Wheel Set Bearing Fault of High Speed Locomotive

·作者： 李紫鹏，陈景龙，訾艳阳，潘骏，王宇

·摘要：As a critical component of high-speed locomotive bogie, wheel set bearing fault identification and prognosis has attracted an increasing attention in recent years. However, heavy background noise and adverse working conditions make it difficult to excavate the hidden weak fault feature from the vibration signal. Variational Mode Decomposition(VMD), which can segment the Intrinsic Mode Functions from the non-stationary signal adaptively and non-recursively, brings a feasible method. However, the inaccurate pre-set mode number may lead to information loss or over decomposition problem. In this paper, an approximate complete VMD method via correlation analysis is proposed to automatically set mode number and combine the similar modes. To overcome the information loss problem, the appropriate mode number is determined by the criterion of approximate complete reconstruction. Then the similar modes are combined according to the similarity of their envelops to solve the over decomposition problem. Finally, two applications to wheel set bearing fault of high speed locomotive verify the validity of the proposed method.