Special Paper Session #1:

Industrial Big Data Based Prognostics and Health Management

Prognostics and health management (PHM) aims to monitor, assess, detect, and predict anomalies or faults in systems, in which data analytics, fault diagnosis, failure prognosis, and intelligent control are fundamental enabling technologies. With the development of sensing technologies, communication, information and computer, internet of things, machine learning, the amount of data increases rapidly. However, most of the data are not used for PHM. It is desirable to develop big data analytics technologies in PHM to make full use of data. Advances in this area will help to improve the system performance, reduce unexpected downtime, and lower maintenance cost. Therefore, there are growing interests in mining health-related data, which includes not only sensing data, but also include domain knowledge, models, and other related information, under the topic of industrial big data for PHM. The objective of this Special Session on “Industrial Big Data Based Prognostics and Health Management” is to present new ideas, methods, tools, and frameworks for data-driven and knowledge-based intelligent maintenance of industrial systems.

This special session is co-organized by Dr. Xiaohang Jin from Zhejiang University of Technology, China, Dr. Yu Wang from Xi’an Jiao Tong University, China, and Dr. Bin Zhang from The University of South Carolina, USA.

 

Session Organizers:

Dr. Xiaohang Jin
Associate Professor, PhD
College of Mechanical Engineering
Zhejiang University of Technology
288 Liuhe Road, Xihu District, Hangzhou, China 310023
Email: xhjin@zjut.edu.cn

Dr. Yu Wang
Associate Professor, PhD
School of Mechanical Engineering
Xi’an Jiao Tong University
28 Xianningxi Road, Xi’an, China 710049
Email: ywang95@xjtu.edu.cn

Dr. Bin Zhang
Associate Professor, PhD
Department of Electrical Engineering
University of South Carolina
301 Main St. Columbia SC 29208
Email: zhangbin@cec.sc.edu