Ruqiang Yan (Fellow, IEEE) received the M.S. degree in precision instrument and machinery from the University of Science and Technology of China, Hefei, China, in 2002, and the Ph.D. degree in mechanical engineering from the University of Massachusetts at Amherst, MA, USA, in 2007.,He was a Guest Researcher with the National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA, from 2006 to 2008, and a Professor with the School of Instrument Science and Engineering, Southeast University, Nanjing, China, from 2009 to 2018. He joined the School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China, in 2018. His research interests include data analytics, artificial intelligence, and energy-efficient sensing and sensor networks for the condition monitoring and health diagnosis of large-scale, complex, dynamical systems. Dr. Yan is a fellow of ASME. His honors and awards include the IEEE Instrumentation and Measurement Society Technical Award in 2019, the New Century Excellent Talents in University Award from the Ministry of Education in China in 2009, and multiple best paper awards. He is an Associate Editor of the IEEE SENSORS JOURNAL and an Editorial Board Member of the Chinese Journal of Mechanical Engineering. He will be the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement starting from January 2022.
Speech Title: AI-Powered Aero-engine Fault Diagnosis
Abstract: The new generation AI technology, especially deep learning, has shown great advantage in feature learning and knowledge mining, which provides a new way for intelligent diagnosis and prognosis in manufacturing. This talk first provides a brief overview of deep learning. Then applications of some typical deep network models in intelligent diagnosis and prognosis are discussed, followed by a newly designed wavelet kernel deep neural network for aero-engine fault diagnosis.