澳门威斯尼斯人国际|首页

中文版 web

Latest News

Liu Ruonan wins 2020 IEEE TII Outstanding Paper Award

 Campus


In October, IEEE Industrial Electronics Society (IEEE IES) announced the significant award of the last year. Associate Prof. Liu Ruonan from Tianjin University’s College of Intelligence and Computing was awarded the 2020Outstanding Paper Awardby IEEE Transactions on Industrial Informatics, a top journal of IEEE Industrial Electronics Society, for her paper titledSimultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint-Loss Conventional Neural Network.

IEEE Transactions on Industrial Informatics (IEEE TII) is a top international journal with an impact factor of 10. 215, which focuses on factory automation, computing and control systems, human machine interface techniques from a more holistic perspective. Its global journal influence ranks 3rdin the field of computer science and interdisciplinary subjects and 1stin the field of industrial engineering (from International Journal of Research). This award was established by the Institute of Electrical and Electronics Engineers (IEEE). Each year, 1 ~ 2 excellent papers will be selected from nearly 1,000 accepted papers in IEEE TII to recognize the outstanding scientific and technological work based on a comprehensive consideration of the innovation, professionalism and application value, etc.

With the arrival of Industry 4.0, high-end equipments have been widely applied in modern industry, including wind turbines, gas turbines and aero-engines. However, many factors have restrained their safety and efficiency operation, such as the high failure-rate and maintenance cost, the lack of life-time data and operational information, and so on. Considering the inherent correlation between fault diagnosis task and remaining useful life prediction task, a joint-loss convolutional neural network is proposed in the awarded work for operational information sharing, which can enhance the effective fusion of knowledge and data by implicit data augmentation, attention focusing and representation bias algorithms. Through the analysis of a real engineering dataset, it has been proven that the proposed method provides an effectiveforecast maintenance strategy for high-end equipment and will further promote the informatics, digital and intelligent transformation of modern industry in the age of intelligent manufacturing.

This work has been reported by IEEE Industrial Electronics Society, and has attracted great attention from scholars at home and abroad.

Currently, Ruonan Liu is an Associate Professor of College of Intelligence and Computing, Tianjin University. Her research mainly focuses on intelligent and dynamic unmanned systems, interpretable AI, intelligent maintenance and remaining useful life prediction of high-end equipment, etc. She has published more than 20 papers in recent years. More than 10 of them have been published in top journals, and 4 of them have been selected as ESI highly cited papers. She also serves on the editorial board of journals, including Frontiers in Artistic Intelligence, Shock and Vibration and Sustainable Energy Technologies and Assessments (Impact Factor=5. 35) as Associate Editor, and the Session Co-Chair of the 5thAsian Conference on Artificial Intelligence Technology (ACAIT 2021).

By: College of Intelligence and Computing

Editor:Qin Mian & Yang Fan