Fault Diagnosis and Predictive Maintenance

Introduction: With the increase in the degree of intelligence and complexity of industrial equipment, the traditional “repair after the fact” and “regular maintenance” model has been difficult to meet the stringent needs of modern industry for safety, reliability and economy. In intelligent manufacturing, new energy, aerospace and other fields, equipment failure may lead to significant economic losses or even safety accidents, while excessive maintenance will significantly increase operating costs. In recent years, digital transformation centered on artificial intelligence, big data, and the Internet of Things (IoT) has provided a new methodology for fault diagnosis and predictive maintenance technology. Real-time sensing of equipment status through multi-source sensing data, combined with advanced technologies such as deep learning and digital twins, can accurately identify early failure characteristics, predict remaining service life, and dynamically optimize maintenance strategies. This technological paradigm innovation is driving industrial O&M from “reactive response” to “proactive defense”, becoming the core support for Industry 4.0 and smart maintenance.

This topic focuses on the cutting-edge theories of fault diagnosis and predictive maintenance, and aims to exchange the recent research results in this field.

Organizers:

	Dongnian Jiang, Lanzhou University of Technology, China Dongnian Jiang, Ph.D., Professor, Doctoral Supervisor, Member of the Committee on Fault Diagnosis and Safety of Technical Processes, Member of the Committee on Predictive Control and Intelligent Decision Making, and supported by Gansu Province “Longyuan Young Talents” Talent Program and Gansu Province Outstanding Youth Fund. He received his undergraduate degree from Xiamen University in 2006, PhD degree in Control Theory and Control Engineering from Lanzhou University of Science and Technology in 2018, and postdoctoral research work in Gansu Electric Power Research Institute from 2019 to 2022. He is mainly engaged in the research of artificial intelligence, intelligent sensor design and fault diagnosis. In recent years, he has presided over two projects of the National Natural Science Foundation of China, and undertaken more than 20 projects including the National Key Research and Development Program, Gansu Outstanding Youth Fund Project, Gansu Provincial Key Research and Development Program, and Gansu Provincial Natural Science Foundation Key Projects. He has published more than 40 academic papers in IEEE Transactions on Reliability, ISA Transactions, Measurement and other journals.
	Huichao Cao, Lanzhou University of Technology, China Huichao Cao, Ph.D., Associate Professor, Member of the Industrial Big Data and Intelligent System Branch Committee of Gansu Mechanical Engineering Society, Special Expert of Jin Chuan Group Information & Automation Engineering CO.,LTD. She received her Ph.D. degree in Control Theory and Control Engineering from Lanzhou University of Technology in 2015. Her primary research focuses on safety assessment of dynamic systems, system fault diagnosis and fault-tolerant control. In recent years, she has presided over one provincial Natural Science Foundation project and one open fund project from the Provincial Key Laboratory of Advanced Control. As a key member, she has participated in more than ten national and provincial projects. She has been awarded two Second Prizes of Provincial Science and Technology Progress, one Second Prize of Provincial Teaching Achievement, and has published more than 30 scientific research and teaching papers.
	Haijie Mao, Lanzhou University of Technology, China Haijie Mao, Ph.D., Associate Professor . She received her undergraduate degree from Taiyuan University  of Technology in 2001, PhD degree in Control Theory and Control Engineering from Lanzhou University of Science and Technology in 2018. Her primary research focuses on fault diagnosis and fault-tolerant control of dynamic systems, life prediction and health maintenance of control systems, advanced industrial process control, and robotics control.In recent years, she has led or served as a key technical lead on over 10 projects, including those funded by the National Natural Science Foundation of China (NSFC), Provincial Natural Science Foundations, the Gansu Provincial Key Laboratory of Advanced Control, and various corporate-sponsored projects. She has published more than 30 research and teaching papers.

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