题目：Intelligent Systems for Machinery Health Condition Monitoring
时间：2020年1月2日15:00-16:30
地点：机械楼243会议室
报告人：Wilson Wang 教授（Lakehead University, 加拿大）
简介：
Wilson Wang，工学博士(University of Waterloo)，加拿大湖首大学(Lakehead University)机械系终身教授,湖首大学学术带头人(Research Chair).加拿大科研基金(NSERC)委员会机械部主任委员(2016-2019)，国际工程设备管理学会会士 (Fellow of ISEAM),IEEE高级会员(Senior Member of IEEE).王教授获得诸多奖项，近期奖励包括加拿大科技委员会科技发展奖(NSERC Accelerator Award 2016)，湖首大学杰出科研成就奖(Distinguished Researcher Award 2017)，湖首大学优秀教学奖(Outstanding Teaching Award 2018).王教授发表论文150余篇，出版专著5部。主要研究方向：机电一体化，故障诊断，人工智能，信号处理等。
摘要(Abstract):
Reliable real-time condition monitoring systems are critically needed in industries to recognize initial equipment defects so as to improve production quality, operation efficiency and safety, but to reduce costs. An intelligent monitoring system consists of modules such as data acquisition, signal processing, diagnostics and prognostics. In real-world industrial monitoring applications, smart sensor-based data acquisition systems should be used to collect signals wirelessly. Signal processing is a process to extract representative features from measurement for system analysis and fault detection in machinery units such as gears and bearings. The related signal processing techniques should be robust to noise and sensitive to health associated features. Diagnosis is a procedure to classify features/patterns into different categories corresponding to different equipment health states. Soft computing tools such as neural fuzzy schemes and clustering methods have been commonly used in automatic diagnostic classification. Prognosis is a process to forecast future states of a dynamic system for remaining useful life prediction. Appropriate machine learning algorithms can be used to improve decision-making convergence and adaptive capability to accommodate different machinery conditions.
This speech will discuss the research and development in these areas, the related challenges and possible solution strategies.
