Special Session: Mechanical Signal Processing and Intelligence Fault Diagnosis of Industrial Equipment
Industrial equipment is prone to failures under complex and harsh working environments, and it is a critical and fascinating field to detect and recognize fault patterns in time for safe and reliable operations. Mechanical signal processing is one most important strategy to perform fault diagnosis through advanced feature extraction and intelligence pattern recognition. Due to the recent rapid developments in machine learning and deep learning, many novel mechanical signal processing models and algorithms are developed and state-of-the-art diagnostic accuracy is achieved. This invited session aims to provide a platform for academic and industrial communities to report recent research and development on fault diagnosis with theoretical and/or applied nature. Suitable topics for this special session include but are not limited to:
Sparse representation based diagnostic methods
Sparse learning in feature detection
Sparse index design for health monitoring and prognosis
Impulsive blind deconvolution
Advanced time scale/frequency analysis
Non-linear time series analysis
Physics-enhanced machine learning
Optimization inspired deep diagnostic methods
Intelligent health monitoring and prognosis
Submission Deadline: June 15, 2023
Notification: July 10, 2023
Registration: July 25, 2023
Submit Method: please send your manuscript to email@example.com with subject "Submit+Special Session-Mechanical+Paper Title". (请通过邮件发送稿件，邮件题目：Submit+Special Session-Mechanical+Paper Title)
Prof. Xuefeng Chen, Xi'an. Jiaotong University, Xi’an, China
Biodata: Prof. Xuefeng Chen is a Full Professor of the School of Mechanical Engineering, Xi'an. Jiaotong University, Xi’an, China. He is a member of American society of mechanical engineers (ASME) and the Chair of the IEEE Xi’an and Chengdu Joint Section Instrumentation and Measurement Society Chapter. He has authored over 100 publications in the areas of composite structure, aero-engine, wind power equipment.
Prof. Yi Qin, Chongqing University, Chongqing, ChinaBiodata: Yi Qin is a Full Professor of the the College of Mechanical and Vehicle Engineering and State Key Laboratory of Mechanical Transmission at Chongqing University, Chongqing, China. He is the Young top-notch talent of China. He is also selected as World’s Top 2% Scientists 2022 by Stanford University and Elsevier. His current research interests include signal processing, intelligent fault diagnosis and prognosis, digital twin. He has undertaken a number of projects, such as National Key R&D Program of China, National Natural Science Foundation of China, et al. He has published a monograph and over 140 papers in journals and conferences. He has got 23 national invention patents, and several international prizes. He is an Editor of several journals, and an editorial board member of Journal of Vibration, Measurement and Diagnosis, and Journal of Dynamics, Monitoring and Diagnostics. He is the senior member of IEEE and the vice chair of IEEE Reliability Society Chongqing Chapter.
Dr. Zhaohui Du, Northwestern Polytechnical University, Xi'an, China
Dr. Zhaohui Du currently works in the School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China. In 2016, he studied optimization theory in Department of Mathematics, University of California, Los Angeles. His general research interests lie in the areas of machine learning, sparse optimization theory and array signal processing for the fault diagnosis of key mechanical system and ocean signal processing. He has authored over 30 SCI publications, and was the recipient of Best Paper Awards of Signal Processing 2019.