Special Session: Sparse Representation and Deep Learning for Mechanical Fault Diagnosis

Fault detection and diagnosis is an important strategy to detect and recognize the underlying failures for the operational safety of mechanical systems. In recent years, sparse representation is a popular model-driven tool to detect fault features and deep learning is a typical data-driven strategy to recognize fault category. Many novel diagnosis strategies combined sparse representation and deep learning are developed and achieved satisfying accuracy and interpretability. This invited session aims to report recent research works about the intersection points in the combination strategy for mechanical fault diagnosis with theoretical framework or industrial applications. Interesting topics for this special session include but are not limited to:


Sparse representation based diagnostic methods
Sparse index design for health monitoring and prognosis
Impulsive blind deconvolution
Cyclostationarity analysis
Physics-Informed Neural Networks for fault detection
Signal processing method driven machine learning
Plug-and-Play fault feature detection methods
Optimization inspired deep diagnostic methods
Intelligent health monitoring for shaft/bearing/gear
Aero-engine fault diagnosis
Underwater machinery fault detection

Submission Deadline: July 10, 2024
Notification: August 05, 2024
Registration: August 20, 2024
Submit Method:
1, submit it via the link: http://confsys.iconf.org/submission/icicsp2024 (after entering the link, click on the corresponding topic)
2, send your manuscript to icicsp@vip.163.com with subject "Submit+Special Session-5+Paper Title". (请通过邮件发送稿件,邮件题目:Submit+Special Session-5+Paper Title)

CHAIRMANS:

Assoc. Prof. Han Zhang, Chang’an University, Xi’an, China

Biodata: Han Zhang is a full associate professor of the School of Construction Machinery at Chang’an University, Xi’an, China. She is currently a member of the Chinese Society of Vibration Engineering and undertake Chang’an scholar project. Her research interests include sparse optimization theory and vibro-acoustic signal processing methods for aero-engine fault diagnosis. She has hosted the National Natural Science Foundation of China, Key Research Development Program of Shaan'xi Province and many enterprise application projects. She has published over 40 high-quality papers in journals and conferences, and won many prizes, include the Best Paper Awards of Signal Processing 2019, Best Paper Awards of 6th International Conference on Information Communication and Signal Processing, Frontrunner 500 Awards of Top Articles in Outstanding S&T Journals of China.

Prof. Yi Qin, Chongqing University, Chongqing, China

Biodata: Prof. Yi Qin is a Full Professor of 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 190 papers in journals and conferences. He has got 35 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.

Prof. Gang Tang, Beijing University of Chemical Technology, China

Biodata: Prof. Gang Tang is currently a Professor with the School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, National Key Laboratory of High-end Compressor and System Technology, and Key Laboratory of Engine Health Monitoring and Networking, Ministry of Education. He is currently a member of the Dynamic Signal Analysis Special Committee and the Fault Diagnosis Special Committee of the Chinese Society of Vibration Engineering, the Youth Work Committee of the Chinese Artificial Intelligence Society, and the Deputy Editor in Chief of IEEE OJIM Journal. He has been selected as one of the Hundred Youth Talents of Beijing University of Chemical Technology, and has been awarded as the " Youth Teaching Master of National Petroleum and Chemical Education". His research interests include mechanical fault diagnosis and intelligent operation and maintenance technology. In recent years, he has published over 40 high-quality papers, participated in drafting 4 national standards; Hosted national key research and development projects, National Natural Science Foundation of China, and enterprise application projects. The relevant achievements have been successfully applied to the intelligent operation and maintenance of high-end equipment such as aerospace, aviation, and navigation. He has won the second prize of China National Petroleum Corporation's Technology Invention Award and the second prize of China Hospital Association's "Anti epidemic Informatization" Award.

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.