Prof. Xinyu LiVice Dean of the School of Mechanical Science and Engineering,
Huazhong University of Science and Technology, China
Xinyu Li, PhD, Professor, Vice Dean of the School of Mechanical Science and Engineering, Vice Director of the State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology. He has awarded the Young Changjiang Scholars Program of the Ministry of Education. His research directions focus on the intelligent manufacturing system and big data analysis. He acts as the PI of more than 10 national projects, including National Key R&D projects and NSFC projects. He has published more than 100 SCI papers, including 6 ESI hotspot papers and 18 ESI highly-cited papers. He is the Associate Editor of ESWA, IET CIM and other journals, as well as the Secretary-General of Industrial Big Data and Intelligent Systems Branch of Chinese Mechanical Engineering Society.
Title:Data-driven Intelligent Manufacturing System
Abstract: With the rapid development of IoT and artificial intelligence, data-driven intelligent manufacturing system has become a trend in both academia and industry, especially the breakthrough of deep learning showing great potential in intelligent manufacturing. This topic will focus on the development trend of data-driven intelligent manufacturing systems, and present some key technologies, such as deep learning-based defect inspection and fault diagnosis. Firstly, the development process of intelligent manufacturing and how the data works in the manufacturing system are discussed. Secondly, two key technologies for fault diagnosis and defect inspection are introduced, which include the proposed methods and practical applications. Finally, the challenges and development directions are presented.
Prof. Shaobo Li
Director of the State Key Laboratory, Guizhou University, China
Li Shaobo graduated from Chengdu Institute of Computer Application, Chinese Academy of Sciences in June 2003, and received his Doctor of Engineering degree in Computer Software and Theory from Graduate School of Chinese Academy of Sciences. He is currently a member of the Party Committee of Guizhou University and director of the State Key Laboratory of Public Big Data. Main research areas: public big data integration and integration, intelligent manufacturing, etc. He has won the honorary titles of New Century Outstanding Talents of the Ministry of Education, Guizhou Core Expert, provincial management expert, Provincial hundred level innovative talents, Provincial Outstanding Young scientific and technological talents, and provincial excellent doctoral supervisor. He is the doctoral supervisor of Software Engineering and Mechanical Engineering of Guizhou University, and the part-time doctoral supervisor of computer software and theory of University of Chinese Academy of Sciences.
In the past 5 years, he has presided over more than 20 scientific research projects of various kinds, including: 3 National Natural Science Foundation projects (1 major research plan cultivation project, 2 surface projects), 1 national key research and development plan project, 1 national key research and development plan sub-task, and 2 national intelligent manufacturing new model application projects; He has published more than 120 papers, including 94 indexed by SCI and 27 indexed by EI. He has obtained 25 authorized invention patents, 36 software copyright registrations, and published 1 textbook, 3 monographs and 1 translated book. Won one special prize and one third prize of Guizhou Graduate Teaching Achievement Award; Provincial teaching Achievement Award 1 special prize, 1 first prize; Provincial science and technology progress 2 second prize, 1 third prize.
Title: Big Data Governance Integration and Typical Industry Applications
Abstract: Currently, data governance has become a key development technology for big data infrastructure, as well as a hotspot in the big data industry ecosystem, and even more of a core concern for enterprises. In big data governance, digital networking will become a new type of information infrastructure in the digitalization era, and big data standards and norms and software and hardware ecosystems centered on open source communities will become the focus of development. This report covers three aspects: big data integration governance, boosting data factorization and regional governance, and assisting the transformation and upgrading of traditional featured industries, to promote the in-depth integration of regional big data and the real economy, to promote the development of big data regional innovation and new models.
Assoc. Prof. Pinjia Zhang
winners of National Science Fund for Outstanding YoungScholar, Tsinghua University, China
Pinjia Zhang, currently a professor of the department of the electrical engineering of the Tsinghua University, the doctoral advisor, the receiver of The National Science Fund for Distinguished Young Scholars. In 2015, he was founded by the National Youth Thousand Talents Program. In 2018, he was funded by the Outstanding Youth Fund of the National Natural Science Foundation of China. In 2018, he was the first mainland winner to receive the IAS Andrew W. Smith Outstanding Young Member Achievement Award. In 2019, he was awarded the "Academic Newcomer Award" by Tsinghua University. In 2021, he won the "Zhongda Young Scholar" award, won the first prize of the Science and Technology Award of the China Electrotechnical Society as the first receiver, and the gold medal of the Geneva International Exhibition of Inventions. He has been engaged in research on online monitoring and fault diagnosis of electrical equipment for a long time. He serve as the editorial board member of IEEE Transactions on Industrial Electronics, IEEE Transactions on Industry Applications and other journals. He also serve as the chairman and convener of the CIGRE/A1.45 Motor System Online Monitoring Standards Committee, and participated in the organization of 4 IEEE standards. He has been published more than 40 papers as the first/corresponding author in various IEEE transactions, and won the IEEE Transactions on Energy Conversion Best Paper Award, three times won the first prize paper of IEEE Industrial Application and Industrial Electronics Society Electrical Engineering Committee.
Title:The Intelligent Monitoring Technology for the Electrical Machine System
Abstract: The electrical machine system is the key equipment of the modern industry, which is important for the aim of the carbon neutrality and emission peak. In addition, various cutting-edge applications pose challenges to the reliability of motor systems under complex operating conditions. Therefore, the researches about intelligent monitoring technology are carried out from electrical, mechanical, thermal and other aspects, in order to realize high precision intelligent perception of multiple physical quantities based on electromagnetic signals. First, the prognosis method for insulation fault based on leakage current measurement is proposed. The magnetic filter core is designed based on the difference between leakage current and load current in magnetic path. The high-accuracy leakage current sensing technology is realized which can be used for the online monitoring and fault prognosis of insulation. Second, the intelligent monitoring method for the drivetrain based on the electromagnetic torque is proposed. The external sensing method based on vibration signal is changed into the internal sensing method based on electromagnetic torque, which transforms the electrical machine from “power heart” into “perception”. The fault diagnosis can be achieved without extra vibration sensor. Third, the maximum capacity control method based on the thermal monitoring is proposed. Based on the high-accuracy thermal monitoring method for the stator and the rotor of electrical machines and power electronics, the method can maximize the output power of electrical machine within the thermal constraint using the electric-magnetic-thermal coupling model.
Prof. Hui-Hwang Goh
IET Fellow, AAET Fellow, Guangxi University, China
Professor Ir. Dr. Hui-Hwang Goh, a renowned consultant in electrical engineering, has conducted extensive research. He is also a fellow of the ASEAN Academy of Engineering and Technology (AAET), a foreign fellow of the Chinese Society for Electrical Engineering (CSEE), and a fellow of the Institution of Engineers, Malaysia (IEM) for his contributions to the advancement of electrical engineering. He is registered with the Engineering Council of the United Kingdom (ECUK) and the Board of Engineers, Malaysia (BEM) as a Chartered and Professional Engineer. He is a Senior Member of IEEE-USA, an ASEAN Chartered Professional Engineer (ACPE), an APEC Engineer, and an International Professional Engineer under IPEA. Prof. Goh is an expert in power electronics, power systems, motor control, renewable energy, and multi-energy conversion. He has over one hundred publications with peer review, eight book chapters, and four international invention patents. He championed energy cooperation between China and ASEAN.
Title: Applications of 5G and Internet of Things (IoT) in the Energy Sector
Abstract: Internet of Things (IoT) and 5G are utilised in smart energy to facilitate cost-effective and efficient power delivery. This hybrid energy system integrates renewable energy sources such as solar and wind with Internet of Things (IoT) and 5G applications and devices. Smart energy systems include a variety of energy sources and types, production and delivery infrastructures, operational processes, and use cases in addition to smart electrical circuits and metres. By providing customers with an accurate view of their energy consumption, IoT-based smart instruments enable utilities to increase efficiency, reduce costs, and improve service. You can monitor your energy consumption and availability in real time with a smart metre. Using real-time data on energy consumption, utility companies can better allocate their resources and reduce service interruptions. Customers can reduce their carbon footprints and save money as a result of more accurate invoicing and enhanced accounting procedures.
Prof. Guilin Yang
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, China
Dr Guilin Yang is currently a professor, a doctoral supervisor, and the state specially recruited expert. He is also the deputy president of Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, and the deputy chairman of the Robotics Branch of the Chinese Society of Mechanical Engineering. Dr Yang has long been engaged in robotics and intelligent manufacturing technology. He has also led a number of national and provincial scientific research projects in the fields of precision actuators, parallel robots, cable driven robots, omnidirectional mobile robots, collaborative robots, and industrial robot application technologies. He has published more than 360 academic papers and 3 monographs, and authorized over 70 domestic and foreign patents. He received the "R&D 100 Awards" from the United States, the first prize of the China Industry-University Research Cooperation and Innovation Achievement Award, the gold medal of the China "Good Design", and the China Patent Excellence Award.
Title: Advanced Industrial Robotics for Intelligent Manufacturing
Abstract: Industrial robotics are critical enabling technologies for intelligent manufacturing. In this talk, the latest R&D progress on both applied industrial robotics and collaborative robots will be presented. To make the existing industrial robots easy-to-program, much more accurate, and suitable for contact-type operations, the applied technologies, such as intuitive teaching and rapid programming，calibration and error compensation, and contact force control, have been developed. Collaborative robots are a new class of industrial robots that are lightweight, intrinsically safe, and capable of performing a variety of tasks alongside human operators. The key technologies, such as high-performance torque motors, integrated joint modules, and compliant motion control, will be introduced.
2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE 2023) http://icbaie.net/