Auteur: Ruqiang Yan

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Weihua Li, Senior Member, IEEE, received the Ph.D. degree in mechanical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2003. He is currently a Dean and Professor with the School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China. Prof. Li is now serving as the co-chair of Technical Committee (TC-3) on Condition Monitoring & Fault Diagnosis Instrument, IEEE Instrumentation and Measurement Society (IEEE IM Society). He is the director of the Industrial Intelligence Technology Innovation Center of Pazhou Lab, Guangzhou, and the director of the Joint Laboratory of Intelligent Manufacturing between the SCUT and CIMC. He also serves as a member of the Editorial Board of IEEE Trans. on Instrumentation & Measurement (TIM)、IEEE Sensors Journal、Journal of Dynamics, Monitoring and Diagnostics (JDMD)、IET Collaborative Intelligent Manufacturing、Chinese Journal of Mechanical Engineering (CJME) and Journal of Vibration Engineering (JVE). His research interests include Industrial intelligence, Industrial Big Data, Digital Twins, Intelligent Maintenance & Health Management and Environment Perception & Path Planning for Intelligent Connected Vehicles. He is the PI (principal investigator) of nearly 20 projects which are funded by National Natural Science Foundation of China, National Key Research and Development Program of China, Key Research and Development Program of Guangdong Province, University-Industry Cooperation, etc. Prof. Li has published over 110 papers in related journals, including IEEE Trans. on Industrial Informatics、Instrumentation & Measurement、Sensor Journal、IEEE/ASME Mechatronics, Renewable Energy, Mechanical System & Signal Processing, Journal of Mechanical Engineering, etc. In addition, he has published 5 books and issued more than 20 Chinese invention patents.  Xiaoli Zhang received the Ph.D. degree in mechanical engineering from the Xi’an Jiaotong University, Xi’an, China, in 2011. She is currently the associate professor with the School of Construction Machinery, Chang’an University, Xi’an, China. Her research interests include machinery intelligent maintenance and condition monitoring, reliability analysis. Dr. Zhang is the member of Fault Diagnosis Committee of Chinese Society of Vibration Engineering. She is the PI of nine projects which are funded by National Natural Science Foundation of China, National Science and Technology Support Program, Young Talents Promotion Program of Shaanxi University Science and Technology Association, etc. Dr. Zhang has published over 30 papers, issued more than 10 Chinese patents, and published 1 book. Ruqiang Yan received the Ph.D. degree in mechanical engineering from the University of Massachusetts Amherst, Amherst, USA, in 2007. He was a guest researcher at the National Institute of Standards and Technology (NIST) in 2006–2008 and a professor at the Southeast University, China, in 2009–2017. Dr.Yan joined Xi’an Jiaotong University in 2018, and he is an ASME fellow and received Technical Award of the IEEE Instrumentation and Measurement Society in 2019. His research interests include instrumentation design, data analytics, and energy-efficient sensing for condition monitoring and health diagnosis of large-scale, complex, dynamical systems. Prof. Yan is currently an Ad Com member of the IEEE Instrumentation and Measurement Society (IMS) and serves as the vice-president (VP) for Membership. He was also the VP for Technical and Standards Activities of the IMS in 2016–2019. Prof. Yan is the region 10 liaison of the IMS and formed the first local IMS chapter (Nanjing/Shanghai/Wuhan Jt. Sections IMS Chapter) in China, to promote instrumentation and measurement related activities. He is a chair of the Technical Committee (TC-7) on Signals and Systems in Measurement and worked as a working group co-chair to develop an IEEE P1451.001 Standard for Signal Treatment Applied to Smart Transducers. Prof. Yan has been serving as the Editor-in-Chief for the IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT since January 2022; His honours and awards include the IEEE Instrumentation and Measurement Society Technical Award in 2019 and the Distinguished Service Award in 2022, and multiple best paper awards. Prof. Yan has published over 180 papers, issued more than 20 Chinese invention patents, and published 3 books.




6 Ebooks par Ruqiang Yan

Robert X Gao & Ruqiang Yan: Wavelets
Wavelets: Theory and Applications for Manufacturing presents a systematic description of the fundamentals of wavelet transform and its applications. Given the widespread utilization of rotating machi …
PDF
Anglais
€171.19
Ruqiang Yan & Xuefeng Chen: Structural Health Monitoring
This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse repres …
PDF
Anglais
€149.79
Weihua Li & Xiaoli Zhang: Intelligent Fault Diagnosis and Health Assessment for Complex Electro-Mechanical Systems
Based on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment …
PDF
Anglais
€160.49
Fei Shen & Ruqiang Yan: Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis
Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis introduces the theory and latest applications of transfer learning on rotary machine fault diagnosis and prognosis. Transfer learnin …
EPUB
Anglais
DRM
€198.60
Ruqiang Yan & Zhibin Zhao: Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions.The authors first introduce basic appl …
PDF
Anglais
DRM
€98.58
Ruqiang Yan & Zhibin Zhao: Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions.The authors first introduce basic appl …
EPUB
Anglais
DRM
€98.86