题 目: Data Mining for Prognostics(数据挖掘与预警技术) 报告人：Dr. Chunsheng Yang， National Research Council (NRC), Canada 时 间：2008年9月25日（周四）上午 10：00 地 点：蒙民伟楼404会议室
Abstract:
Data-driven prognostics is an emerging application of data mining to real-worl
d problems such as system health management. It has been attracting much atten
tion from researchers in the area of sensor, reliability, data mining and so o
n. The main task of prognostics is to predict the likelihood of a failure and
estimate the remaining lifetime (or time to failure). Data-driven prognostics
is to develop predictive models from large-scale historic operational and main
tenance data using the techniques from data mining and machine learning. For t
his purpose, we have developed a KDD methodology to build the prognostic model
s which are able to predict the failure and estimate time to failure. In this
talk, we will introduce the KDD methodology in details by addressing several c
hallenging issues: model building, model evaluation and time to failure predic
tion. We will also present some results obtained from a real-world applicatio
n--prognostics of train wheel by demonstrating the deployed prognostic systems
. Dr. Chunsheng Yang is a Senior Research Officer with the Knowledge Discovery G
roup at the Institute for Information Technology of the National Research Coun
cil Canada (NRC-IIT). He received a Ph.D. from National Hiroshima University,
Japan, 1995. He worked with Fujitsu Inc., Japan as a Senior Engineer from 1995
to 1998. His research interests include data mining for prognostics, case-bas
ed reasoning for diagnostics and multi-agent-based systems for group decision-
making. He serves as a guest editor for the International Journal of Applied I
ntelligence, and is a Program Co-Chair for the 17th International Conference o
n Industry and Engineering Applications of Artificial Intelligence and Expert
Systems (IEA/AIE), an Industrial Track Program Committee Member of ACM SIGKDD
07 and ACM SIGKDD 05. |
