The IASTED International Conference on
Engineering and Applied Science
EAS 2012

Engineering Applications for the 21st Century

December 27 – 29, 2012
Colombo, Sri Lanka


Predictive Maintenance - Zero-Breakdown Machines and Service: Fundamental Knowledge, Emerging Technologies and Case Studies

Dr. Junhong Zhou
SIMTech, Singapore


Prognostics Health Management has become increasingly important over the past decade as a means to maximize the asset usage and plant operating efficiency. The Prognostics Health Management methodology and techniques have attracted strong research interest of the past decade. This tutorial will provide the participants with fundamental knowledge and practical case-studies of Prognostics Health Management and Condition based Maintenance technologies as applied to industry processes. The Intelligent Maintenance Systems framework and architecture, technologies of sensing, signal process, feature extraction and selection and advanced prognostics and predictive analytics will be discussed. Several industry case-studies will provide practical insights into how these emerging technologies are deployed in manufacturing and service industries.


This tutorial will help attendees to:
• Learn and gain insight into the state-of-the-art technologies and systematic tools of Prognostics Health Management.
• Find out through in-depth case studies of how today’s global companies are leading the market as pioneers in predictive manufacturing and service.


• Introduction
• System condition based maintenance:
o Sensors
o Sensing
o Signal processing
o Feature extraction and selection
o Data mining and modeling
• Fault diagnosis and prognosis architecture
• Case studies:
o Wind turbine drive train fault diagnosis
o Tool condition monitoring
o Machine center fault diagnosis

Qualifications of the Instructor(s)

Tutorial Session Portrait

Dr Zhou Junhong obtained her PhD in Nanyang Technological University for her research in advance feature extraction and selection in Condition Based Maintenance. Dr. Zhou is a principle researcher of SIMTech and she is the initiative lead in the area of maximizing overall equipment effectiveness.
Since joining SIMTech in 1996, Dr. Zhou has been actively involved with a number of research and industry projects in the areas of process monitoring & control, sensing & advanced signal processing, data analytical and data mining. Dr. Zhou has been actively worked with industries includes the SCADA system, sensing & measurement for machine tooling condition, intelligent modeling for equipment health prognostics, and process monitoring & product control in manufacturing processes and intelligent systems to maintain serviceability of manufacturing equipment.
Dr. Zhou has published over 50 technical papers and is the recipient of Best Application Paper Award in The 8th Asian Control Conference (ASCC 2011).