Advanced Measurement and Data Processing for Complex Engineering System Health Monitoring
Engineering system health monitoring has attracted increasing attention during the last decade and significant research efforts have been taken by both academia and industry. Dynamical changes of engineering systems have to be captured in time for safe and reliable operations. These tasks are typically realized by using measurement technologies in combination with data processing algorithms. Recent advances in the theory and methodology for measurement and data processing have provided viable tools to dealing with issues encountered in engineering system health monitoring. This invited session aims to provide a platform for academic and industrial communities to report recent research and development on engineering system health monitoring with theoretical and/or applied nature. Suitable topics for this special session include but are not limited to:
- New measurement methodology for system health monitoring
- Wireless sensor networks for distributed measurement
- Advanced time scale/frequency analysis
- Intelligent health monitoring and prognosis
- Non-linear time series analysis
- Other-related topics
Dr. Weihua Li, South China University of Technology, China
Dr. Ruqiang Yan, Southeast University, China
Dr. Xuefeng Chen, Xi’an Jiaotong University, China
Organizers and Contact Information
Dr. Weihua Li, South China University of Technology, China (email@example.com)
Dr. Ruqiang Yan, Southeast University, China (firstname.lastname@example.org)
Dr. Xuefeng Chen, Xi’an Jiaotong University, China (email@example.com)
Engineering System Health Monitoring: An Advanced Signal Processing Perspective
Machine condition monitoring and fault diagnosis plays an important role in modern industry, which used to ensure safe production, improve efficiency, reduce downtime and prevent failures. As a means of sensing the pulse of a complex engineering system, measurement technology and data processing attracted more and more attentions from both academia and industry.
During the past decades, vibration analysis, acoustic emission, and image processing were adopted in machine health monitoring, and various data processing methods were explored for fault diagnosis and remain useful life prediction. For example, Lu proposed a contactless angular resampling method for bearing fault diagnosis by using video signals and acoustic signals. The acoustic signal is demodulated to obtain the fault characteristics frequency whereas the video signals is utilized for tracking instantaneous rotating angle. Van proposed a wavelet kernel discriminant analysis to analyse the bearing vibration signals for fault classification. Bai applied a NSCT-based infrared image enhancement method for rotating machinery fault diagnosis. Many research works have also been reported on I2MTC2015[4-7], I2MTC2016[8-12], and I2MTC2017[13,14].
Moreover, the rapidly growing engineering system trend has been toward intelligence, complex and automation, and dynamical changes of engineering systems become more complicated, which makes machinery health monitoring more important than ever before.
Recent advances in the theory and methodology for measurement and data processing have provided viable tools to dealing with issues encountered in engineering system health monitoring. New measurement technology and new data processing methods provide more precise information to know the health status and the operation of the engineering system.
Therefore, we invite the researchers from both academia and industry to present their latest works on “advanced measurement and data processing for complex engineering system health monitoring”.
Weihua Li (IEEE IM Member, 2012) received the Ph.D. degree in mechanical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2003. He is currently a Professor with the School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China. His current research interests include nonlinear time series analysis, dynamic signal processing, and machine learning methods for condition monitoring and health diagnosis of complex dynamical systems. He is a member of the ASME.
Ruqiang Yan (IEEE IM Member,2007, Senior Member 2011) received his Ph.D. degree in mechanical engineering from the University of Massachusetts Amherst, Amherst, MA, USA, in 2007. He joined the School of Instrument Science and Engineering at the Southeast University, Nanjing, China, as a Full Professor in October 2009. His research interests include nonlinear time-series analysis, multidomain signal processing, and energy-efficient sensing and sensor networks for the condition monitoring and health diagnosis of large-scale, complex, dynamical systems. Dr. Yan is an Associate Editor of the IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. He received the New Century Excellent Talents in University Award from the Ministry of Education in China, in 2009. He is a member of the ASME.
Xuefeng Chen (IEEE IM Member, 2012) received the Ph.D. degree from Xi’an Jiaotong University, Xi’an, China, in 2004. He is currently a Professor of mechanical engineering with Xi’an Jiaotong University, Xi’an. His current research interests include finiteelement method, mechanical system and signal processing, diagnosis and prognosis for complicated industrial systems, smart structures, aeroengine fault diagnosis, and wind turbine system monitoring. Dr. Chen was a recipient of the National Excellent Doctoral Dissertation of China in 2007, the Second Award of Technology Invention of China in 2009, the National Science Fund for Distinguished Young Scholars in 2012, and a Chief Scientist of the National Key Basic Research Program of China (973 Program) in 2015. He is the Chapter Chairman of the IEEE Xi’an and Chengdu Joint Section Instrumentation and Measurement Society.