Sensors Webinar | Machine Health Monitoring and Fault Diagnosis Techniques
Sensors Webinar | Machine Health Monitoring and Fault Diagnosis Techniques
Part of the Sensors Webinar Series series
rotational machine monitoring and vibration signal processing, intelligent early fault detection and diagnosis, fewshot sample learning for fault detection, feature construction with intelligent algorithms, dataefficient domain adaptation and transfer learning, interpretable deep learning for fault diagnosis, data augmentation techniques for fault diagnosis, sensor data fusion for fault diagnosis, measurement methods technologies and systems for fault diagnosis
Welcome from the Chair
5th Sensors Webinar
Machine Health Monitoring and Fault Diagnosis Techniques
Dear Ladies and Gentlemen, Friends, and Colleagues,
We look forward to welcoming you in this webinar entitled “Machine Health Monitoring and Fault Diagnosis Techniques”.
Machine health monitoring is a scorching topic for monitoring machine health conditions, and it helps machines being operated in a safe, economic-saving environment and help plants improve their manufacturing efficiency. Condition monitoring and fault diagnosis techniques provide a guarantee to evaluate machine health conditions. Besides, artificial intelligence algorithms, such as machine learning, deep learning, and transfer learning, are becoming increasingly important to automatically handle and analyze big sensor data and interpret what big sensor data tell us about machine health conditions.
Recently, with a rapid development of advanced artificial intelligence techniques and increasing demands for monitoring machine health conditions with state-of-art AI techniques, diagnostic network structures to handle sensor data, new opportunities, and cutting-edge research have emerged in AI-based machine diagnosis, prognosis, as well as health management.
Today, we are pleased to introduce two recognized experts. Prof. Tangbin Xia at Shanghai Jiao Tong University, whose major research areas are intelligent machine fault diagnosis, intelligent manufacturing system, quality and reliability engineering. Prof. Xiang Li at Xi’an Jiaotong University, whose major research areas include machine learning, fault diagnosis, deep learning, and transfer learning. Prof. Xia and Prof. Li will give us two excellent and attractive talks about advances in Machine Health Monitoring and Fault Diagnosis Techniques in the MDPI Sensors webinar.
Date: 10 August 2022
Time: 3:00 pm CEST | 9:00 am EDT | 9:00 pm CST Asia
Webinar ID: 819 0423 9471
Webinar Secretariat: sensors.webinar@mdpi.com
Chair
Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, China
Dong Wang received the Ph.D. degree from the City University of Hong Kong, Hong Kong, in 2015. He was a Senior Research Assistant, a Postdoctoral Fellow, and a Research Fellow with the City University of Hong Kong. He is currently an Associate Professor with the Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China, where he is also with the State Key Laboratory of Mechanical System and Vibration. His research interests include intelligent operation and maintenance, sparsity and complexity measures, degradation modeling, statistical probability models, signal processing, machine learning and statistical learning. Dr. Wang is an Editorial Board Member for Mechanical Systems and Signal Processing, and an Associate Editor for the IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal, Measurement, and Journal of Dynamics, Monitoring and Diagnostics.
Invited Speakers
Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, China
Tangbin Xia received Ph.D. degree in Mechanical Engineering (Industrial Engineering) from Shanghai Jiao Tong University in 2014. He worked as a postdoctoral in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology and a joint Ph.D. student in the S. M. Wu Manufacturing Research Centre at the University of Michigan. He is currently an Associate Professor and serves as Deputy Director of the Department of Industrial Engineering & Management, as well as Vice Director of Master of Engineering Management (MEM) Center at Shanghai Jiao Tong University. He has published more than 70 high-level journal papers, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, Mechanical Systems and Signal Processing, Reliability Engineering & System Safety, etc. His research interests are intelligent maintenance systems, prognostics & health management, and advanced manufacturing. Dr. Xia is an Editorial Board Member for Energies, and Shock and Vibration, as well as Topic Editor for Materials, Sensors, Applied Sciences and so on.
Dr. Xiang Li is an associate professor at School of Mechanical Engineering, Xi’an Jiaotong University, China. Prior to joining Xi’an Jiaotong University, he was a postdoctoral fellow at University of Cincinnati, US, and a visiting scholar at University of California at Merced, US. He obtained both his PhD and BS degrees in Tianjin University, China. His research interests include industrial artificial intelligence, industrial big data, intelligent machine maintenance, fault diagnosis and prognosis. He has published more than 30 high-level journal papers, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Electronics, IEEE Transactions on Industrial Informatics, etc. He has published 13 ESI highly cited papers and 4 ESI hot papers. His citations in Google Scholar are beyond 3800 with an H-index of 29. His research works have been well recognized by many famous scholars all over the world, and also successfully applied in the real industries such as intelligent manufacturing, etc. He is the early career editorial board member of IEEE/CAA Journal of Automatica Sinica, Journal of Dynamics, Monitoring and Diagnostics, etc.
Webinar Content
Program
|
Speaker/Presentation |
Time in CEST |
Time in CST (Asia) |
|
Dr. Dong Wang Chair Introduction |
3:00 - 3:10 pm |
9:00 - 9:10 pm |
|
Dr. Tangbin Xia Progressive O&M Methodology for Global Service-Outsourcing Network With Dynamical Prognostic Updating |
3:10 - 3:40 pm |
9:10 - 9:40 pm |
|
Dr. Xiang Li Generalized Transfer Learning in Machinery Fault Diagnosis |
3:40 - 4:10 pm |
9:40 - 10:10 pm |
|
Q&A Session |
4:10 - 4:25 pm |
10:10 - 10:25 pm |
|
Closing of Webinar |
4:25 - 4:30 pm |
10:25 - 10:30 pm |
Relevant SI
Guest Editors: Dr. Shilong Sun, Prof. Dr. Changqing Shen & Dr. Dong Wang
Deadline for manuscript submissions: 20 November 2022
Sponsors and Partners
Organizers
