The proceedings of the First International Conference on Equipment Intelligent Operation and Maintenance (ICEIOM 2023) offer invaluable insights into the processes that ensure safe and reliable operation of equipment and guarantee the improvement of product life cycles.
The book touches upon a wide array of topics including equipment condition monitoring, fault diagnosis, and remaining useful life prediction. With special emphasis on the integration of big data and machine learning, the papers contained in this publication highlight how these technologies make the equipment operation process highly automated and ingenious. Intelligent operation and maintenance is set to act as the driving force behind a new generation of smart manufacturing and equipment upgradation, and promote demand for intelligent product services and management.
This is a highly beneficial guide to students, researchers, working professionals and enthusiasts who wish to stay updated on innovative research contributions and practical applications of state-of-the-art technologies in equipment operation and maintenance.
Ruqiang Yan is a Full Professor at the School of Mechanical Engineering, Xi’an Jiaotong University, China. He is engaged in research on theoretical methods and engineering applications related to intelligent operation and maintenance of high-end equipment. Dr. Yan is a Fellow of IEEE (2022) and ASME (2019). He has led the development of one IEEE standard and published over one hundred papers in IEEE and ASME journals, and other publications. Currently, he serves as an IEEE Instrumentation and Measurement Society Distinguished Lecturer and is the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement.
Jing Lin is a professor and Dean of the School of Reliability and Systems Engineering of Beihang University. He is a distinguished professor of the Chang Jiang Scholars Program and an awardee of the National Science Fund for Distinguished Young Scholars. Prof. Lin received his doctor's degree from Xi’an Jiaotong University in 1999. His research interests include machinery dynamic testing and fault diagnosis and industrial big data. Prof. Lin has published over 100 papers in journals, and won a second prize of the State Natural Science Award in 2013. His research has been applied to the fields of energy and power, petrochemistry, equipment manufacturing, rail transit, architecture, biology, electrical engineering and oceanography.
