题 目: Data Mining for Prognostics
(数据挖掘与预警技术)
报告人 ：Chunsheng Yang
National Research Council (NRC),
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-world problems such as system health management. It has been attracting much attention from researchers in the area of sensor, reliability, data mining and so on. 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 maintenance data using the techniques from data mining and machine learning. For this purpose, we have developed a KDD methodology to build the prognostic models 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 challenging issues: model building, model evaluation and time to failure prediction. We will also present some results obtained from a real-world application--prognostics of train wheel by demonstrating the deployed prognostic systems.
Dr. Chunsheng Yang is a Senior Research Officer with the Knowledge Discovery Group at the Institute for Information Technology of the National Research Council Canada (NRC-IIT). He received a Ph.D. from
