Special Session I
Fault Diagnosis and Predictive Maintenance 故障诊断与预测性维护
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.
随着工业装备智能化与复杂化程度的提升，传统“事后维修”和“定期维护”模式已难以满足现代工业对安全性、可靠性与经济性的严苛需求。在智能制造、新能源、航空航天等领域，设备故障可能导致重大经济损失甚至安全事故，而过度维护则会显著增加运营成本。近年来，以人工智能、大数据和物联网为核心的数字化转型，为故障诊断与预测性维护技术提供了新的方法论：通过多源传感数据实时感知设备状态，结合深度学习、数字孪生等先进技术，可精准识别早期故障特征、预测剩余使用寿命，并动态优化维护策略。这一技术范式革新正在推动工业运维从“被动响应”向“主动防御”跨越，成为智能运维的核心支撑。本专题聚焦故障诊断与预测性维护的前沿理论与工程实践，旨在交流相关领域的最新研究成果，为构建自主可控的智能运维理论与技术体系注入创新动力。
Organizers:
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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, 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. |
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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. |
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Haijie MAO, Lanzhou University of Technology, China Mao Haijie, Ph.D.,Associate professor and master's supervisor at the Department of Automation, School of Electrical and Information Engineering, Lanzhou University of Technology. She has been engaged in teaching and research at the university since July 2004. From September 2017 to July 2018, she was a visiting scholar at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. Her primary research interests include fault diagnosis and faulttolerant control of dynamic systems, life prediction and health maintenance of control systems, advanced control of industrial processes, and robotics. In recent years, she has led or served as the main technical lead for over 10 projects, including those funded by the National Natural Science Foundation of China, provincial natural science foundations. She is currently leading one National Natural Science Foundation project and is a key participant in another ongoing National Natural Science Foundation projects. |
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