Special Session List
Special Session #01 Intelligent Maintenance and Reliability of Aviation Equipment
Special Session #02 Data-driven Fault Diagnosis and Fault-Tolerant Control for Complex Industrial Systems
Special Session #03 System Safety Cognition, Diagnosis and Decision Based on Data Intelligence
Special Session #04 Intelligent Brewing Equipment and Quality Control of Baijiu
Special Session #05 Intelligent Fault Diagnosis and Health Management of Mine Electromechanical Equipment
Special Session #06 Intelligent maintenance and fault diagnosis in the railway transportation system
Special Session #07 Real-Time Safety Assessment for Dynamic Systems
Special Session #08 Intelligent nondestructive testing and signal processing
Special Session #09 Research on operating condition recognition, optimal control and intelligent operation and maintenance of complex systems
Special Session #10 Research on abnormal monitoring, fault diagnosis and health management of complex equipment
Special Session #11 Intelligent Fault Diagnosis and Safety Control of Complex Systems
Special Session #12 Fault Diagnosis and Fault-Tolerant Control of Aircraft
Special Session #13 Underwater robot condition monitoring and fault prediction
Special Session #01
Intelligent Maintenance and Reliability of Aviation Equipment
Session Organizers:
Prof. Min Xie, City University of Hong Kong
E-mail: minxie@cityu.edu.hk
Assistant Prof. MinWang, University of Electronic Science and Technology of China
E-mail: mwang@uestc.edu.cn
Download: Special Session #01.pdf
Introduction of Special Session #01
Manufacture procedures of aviation equipment are costly, and also failure mechanism is complex, which results in maintenance of equipment is a difficult task. To improve reliability of aviation equipment, fault diagnosis, remaining useful life prediction and maintenance scheduling methods are discussed for applying to key components such as engines, actuator and control systems in this session.
Special Session #02
Data-driven Fault Diagnosis and Fault-Tolerant Control for Complex Industrial Systems
Session Organizers:
Prof. Hongtian Chen, Shanghai Jiao Tong University
E-mail: hongtian.chen@sjtu.edu.cn
Assistant Prof. Yuchen Jiang, Harbin Institute of Technology
E-mail: yc.jiang@hit.edu.cn
Prof. Hao Luo, Harbin Institute of Technology
E-mail: hao.luo@hit.edu.cn
Download: Special Session #02.pdf
Introduction of Special Session #02
Industrial systems are being developed towards achieving large-scale, highly complex operations with a high degree of automation. This requires the integration of novel functionalities and services through intensive interaction with the environment and external systems. In order to ensure the safety and reliability of critical infrastructures/devices and attain satisfactory performance, there has been extensive research in the field of fault diagnosis and fault-tolerant control (FD/FTC) approaches. The availability of colossal amounts of data containing valuable information about system operating states has made it particularly advantageous to use data-driven approaches. This Special Session is dedicated to exploring innovative data-driven approaches for fault diagnosis and fault-tolerant control of complex industrial systems. It offers a platform for researchers and industrial engineers to share their latest findings on theoretical outcomes, algorithmic innovations, as well as successful practical applications.
Special Session #03
System Safety Cognition, Diagnosis and Decision Based on Data Intelligence
Session Organizers:
Associate Prof. Xinmin Zhang, Zhejiang University
E-mail: xinminzhang@zju.edu.cn
Prof. Ke Zhang, Chongqing University
E-mail: smeta@163.com
Assistant Researcher Zheren Zhu, Zhejiang University
E-mail: prince24@zju.edu.cn
Download: Special Session #03.pdf
Introduction of Special Session #03
How to ensure the intrinsic safety of the process operation system and realize safe production is one of the hot spots that society, government and enterprises pay the most attention to at present. With the development of Internet of Things and intelligent sensing technology, enterprises have accumulated a large amount of multi-source heterogeneous data that characterizes the operating state of the system. To study the security theory and technology of process operating systems by means of data intelligence has been widely concerned and favored by researchers and practitioners. However, there are still many challenges in the cognition, diagnosis and decision-making of the system operation safety based on data intelligence. For example, how to effectively use massive data to clarify the degradation law and fault evolution chain of key components/subsystems; How to discover and extract fault knowledge from data, improve the rapidity and accuracy of fault diagnosis and safety situation assessment, and realize real-time safety monitoring; How to integrate and associate knowledge organically and establish a high-precision security decision-making strategy generation framework. This special session aims to provide a remarkable opportunity for researchers and practitioners to present innovative solutions for Process System Safety Cognition, Diagnosis and Decision Based on Data Intelligence, and to discuss new challenges and future research directions in related fields.
Special Session #04
Intelligent Brewing Equipment and Quality Control of Baijiu
Session Organizers:
Prof. Xingzhong Xiong, Sichuan University of Science & Engineering
E-mail: xzxiong@suse.edu.cn
Associate Prof. Guiyu Zhang, Sichuan University of Science & Engineering
E-mail: gyz_118@163.com
Download: Special Session #04.pdf
Introduction of Special Session #04
Purpose of this Special Session: To discuss the key technologies of intelligent brewing equipment, nondestructive testing technology, intelligent brewing technology and quality control in the process of intelligent brewing of Chinese Baijiu.
Special Session contents: In view of the unique solid-state brewing process of Chinese Baijiu, in the process of transformation from traditional handmade brewing technology to automation, informatization and intelligence. Research and develop special brewing equipment, and use intelligent model instead of traditional artificial experience sensory evaluation, to solve the technical bottleneck of Baijiu industry. Focusing on solid fermented grains, liquid base Baijiu and Solid liquid mixed intermediate product, real-time monitoring and informatization of the brewing process are realized through online rapid non-destructive testing technology. Big data analysis technology is used to mine and construct the intelligent brewing process model, and establish the autonomous decision-making closed-loop control mechanism of brewing equipment. To realize the scientific quality supervision and control of Baijiu brewing process and the health management of equipment. This special session aims to provide a remarkable opportunity for researchers and practitioners to present innovative solutions for Intelligent Brewing Equipment and Quality Control of Baijiu, and to discuss new challenges and future research directions in related fields.
Special Session #05
Intelligent Fault Diagnosis and Health Management of Mine Electromechanical Equipment
Session Organizers:
Fan Hongwei, Associate professor, Xi'an University of Science and Technology
E-mail:hw_fan@xust.edu.cn
Mao Qinghua, Professor, Xi'an University of Science and Technology
E-mail:maoqh@xust.edu.cn
Jiang Kuosheng, Associate professor, Anhui University of Science and Technology
E-mail:jiangkuosheng@aust.edu.cn
Lv Kaibo, Associate professor, Taiyuan University of Technology
E-mail:lvkaibo@tyut.edu.cn
Jiang fan, Associate professor, China University of Mining and Technology
E-mail:jiangfan25709@163.com
Download: Special Session #05.pdf
Introduction of Special Session #05
In recent years, the coal mining technology has advanced from mechanized mining to robot-enabled intelligent mining, which is highly dependent on the continuous and reliable operation of mining equipment. This topic focuses on the “Safe, Reliable, Efficient and Green” running of all kinds of mine electromechanical equipment from the perspectives of basic research, system development and engineering application. The problems of running state monitoring, fault diagnosis, life prediction and health management of mine equipment and their key components (as motor, gearbox, bearing, electrical apparatus, etc.) will be discussed, in particular, focusing on the theoretical innovation and typical application of cutting-edge technologies such as big data analysis, machine (deep) learning, edge/cloud computing and digital twin and vibration, sound, temperature, current, pressure, oil, image, etc.
Special Session #06
Intelligent maintenance and fault diagnosis in the railway transportation system
Session Organizers:
Yuan cao, Beijing jiaotong1 University, Professor
E-mail:ycao@bjtu.edu.cn
Tao wen, Beijing jiaotong1 University, Professor
E-mail:wentao@bjtu.edu.cn
Liang Guo， Southwest Jiaotong University, Associate Professor
E-mail:guoliang@swjtu.edu.cn
Download: Special Session #06.pdf
Introduction of Special Session #06
With increasing requirement of economic and technology, the railway transportation system has achieved great development, especially in High-speed railway. Ensuring operating safety of the railway transportation system is significantly important. Intelligent maintenance and fault diagnosis play key role in keeping safety of the railway transportation system. In this session, we will discuss about new fault diagnosis method of key components in the railway transportation system, such as bogi, point machine, pantograph, rail, etc, and also novel methods for solving problems in data transmission, saving and mining.
Special Session #07
Real-Time Safety Assessment for Dynamic Systems
Session Organizers:
Dr. Xiao HE Deputy Dean Department of Automation, Tsinghua University Beijing 100084, P. R. China，Professor
E-mail: hexiao@tsinghua.edu.cn
Download: Special Session #07.pdf
Introduction of Special Session #07
For high-end complex equipment, such as aviation and aerospace equipment, high-speed train, and deep-sea manned submersible, it may cause irreparable consequences once the accident occurs. Real-time safety assessment refers to determining the system safety status through integrating monitoring data from sensors online, thus assisting in making correct self-rescue decisions. Real-time safety assessment is a necessary tool to evaluate the safety statues of a system to avoid accidents. How to accurately perform real-time safety assessment is a critical challenge for developing intelligent high-end complex equipment and has received significant attention from academia and industry worldwide. Nevertheless, research in this area is relatively scarce. Most existing safety assessment methods are offline-based or rely on expert experience and the precise model. However, the facing practical challenges include, but are not limited to, the unavailable of the precise dynamical model, multi-mode and nonstationary operational characteristics, incomplete training data information, and the open operating environment. This special session aims to explore real-time safety assessment technology for dynamic systems, and it will report on the latest research results in related fields to help promote the development of real-time safety assessment technology, improve the intelligence level of high-end complex equipment, and ensure their safe and reliable operation.
Special Session #08
Intelligent nondestructive testing and signal processing
Session Organizers:
Wu Jianbo, Professor/Department Director, Sichuan University
E-mail: wujianbo@scu.edu.cn
Song Kai, Professor/Dean, Nanchang Hangkong University
E-mail: songkai@nchu.edu.cn
Feng Bo, Lecturer, Huazhong University of Science and Technology
E-mail: bofeng@hust.edu.cn
Tu Jun, Professor, Hubei University of Technology
E-mail: juntu@hbut.edu.cn
Li Erlong, Associate professor, Sichuan University
E-mail: lierlg720@126.com
Yang Yun, Lecturer, Donghua University
E-mail: yun@dhu.edu.cn
Download: Special Session #08.pdf
Introduction of Special Session #08
Non-destructive testing technology is widely used in quality inspection of key components during production and service. With the rapid development of nondestructive testing methods, high-performance sensors and signal processing algorithms, nondestructive testing technology can achieve high-precision evaluation from micro-structure changes to macro-defects. However, in order to meet the extreme working conditions and special performance requirements, new materials, new structures and new working conditions continue to appear, which brings new challenges to NDT technology. With the continuous discovery of new electrical, magnetic, acoustic, optical and thermal physical phenomena, a series of new nondestructive testing theories and methods have been proposed, which is of great significance in breaking through the limitations of traditional methods. On the other hand, the application of new sensing technology, signal processing algorithm and artificial intelligence technology in nondestructive testing can take full advantage of the potential of defect data and realize the identification, classification and quantification of defects. This session aims to provide an exchange platform for NDT researchers to jointly discuss the challenges and future of intelligent NDT and signal processing.
Special Session #09
Research on operating condition recognition, optimal control and intelligent operation and maintenance of complex systems
Session Organizers:
Zhiwen Chen, associate professor, Central South University
E-mail: zhiwen.chen@csu.edu.cn
Qiang Liu, Professor, Northeast University
E-mail: liuq@neu.edu.cn
Bei Sun, associate professor, Central South University
E-mail: sunbei@csu.edu.cn
Kai Zhang, associate professor, Beijing University of Science and Technology
E-mail: kaizhang@ustb.edu.cn
Download: Special Session #09.pdf
Introduction of Special Session #09
At present, the industrial system shows a trend of scale and complexity. However, in practice, it is a challenge to optimize the operation of complex industrial systems in real-time, furthermore, various kinds of faults occur from time to time, resulting in the difficulty of system maintenance and low operation efficiency. In order to improve the efficiency and safety of the running system, many scholars use system theory, big data, artificial intelligence and other technologies to monitor and recognize the operation status of the system, and then realize the optimal control, fault diagnosis, prediction and maintenance strategy of complex systems. This special session will focus on, but not limited to, the latest research results in operating condition recognition, optimal control, fault diagnosis and intelligent operation and maintenance of complex systems, providing a platform and opportunity for participants to discuss, share, exchange and display the results.
Special Session #10
Research on abnormal monitoring, fault diagnosis and health management of complex equipment
Session Organizers:
Qiang Miao, Professor, Sichuan University
Email: mqiang@scu.edu.cn
Fang Xia, Associate Professor, Sichuan University
Email: fangxia@scu.edu.cn
Zhang Dingcheng, Associate Professor, Sichuan University
Email: dc_zhang@scu.edu.cn
Heng Zhang, Assistant Professor, Sichuan University
Email: hengzhang27@scu.edu.cn
Xiaoyan Chu, Assistant Professor, Southwest Jiaotong University
Email: xyanchu@swjtu.edu.cn
Congying Deng, Associate Professor, Chongqing University of Posts and Telecommunications
Email: dengcy@cqupt.edu.cn
Download: Special Session #10.pdf
Introduction of Special Session #10
Currently, modern equipment is becoming increasingly complex, integrated, and intelligent in its structure and function. However, due to harsh work environments, inadequate design verification, production process risks, and the cumulative effects of damage, complex equipment is prone to experiencing abnormal events or failures of varying severity during its life cycle, which could lead to mission failure or even catastrophic consequences. Therefore, scholars at home and abroad are using systems theory, big data, artificial intelligence, and other technical means to achieve real-time monitoring and health state evaluation, timely detection of failure and aging, and improved reliability and safety of complex equipment.
This special session focus on the latest research results in abnormal monitoring, fault diagnosis, and health management of complex equipment, providing a platform for scholars and engineers in related fields to exchange and display their research results, and promoting academic exchange and cooperation.
Special Session #11
Intelligent Fault Diagnosis and Safety Control of Complex Systems
Session Organizers:
Dr. Fangyu Li Professor Faculty of Information Technology Beijing University of Technology Beijing 100124, P. R. China
E-mail: fangyu.li@bjut.edu.cn
Dr. Hongyan Yang Assistant Professor Faculty of Information Technology Beijing University of Technology Beijing 100124, P. R. China
E-mail: Yanghy@bjut.edu.cn
Download: Special Session #11.pdf
Introduction of Special Session #11
With the rapid development of computing, communication, and control technologies, the complexity, automation, and intelligence of industrial systems are increasing, and the security issues of which have received widespread attention. Maintaining the safety of complex systems has become a research focus in the field of fault diagnosis and safety control. Generally speaking, the main causes of abnormalities in complex systems come from system faults and external cyber-attacks. In order to ensure the safety and reliability of complex systems, it is necessary to detect faults and potential safety hazards in the operation of key infrastructure facilities as early as possible, and to minimize the impact of faults. It is difficult to model the mechanism of actual complex systems, which has the characteristics of multiple working conditions and non-stationary, which brings many challenges to the research of fault diagnosis and safety control. Data-driven models can be constructed based on a large amount of available data including operating state information, and supplemented by various intelligent algorithms, which provide a feasible direction for the research of complex systems. This specially-invited symposium aims to discuss intelligent fault diagnosis and safety control technologies of complex systems. The latest achievements in successful and typical practical applications help to promote the development of the field of fault diagnosis and safety control of complex industrial systems.
Special Session #12
Fault Diagnosis and Fault-Tolerant Control of Aircraft
Session Organizers:
Cao Lijia, Associate Professor/ Associate Dean, Sichuan University of Science and Engineering
E-mail: caolj@suse.edu.cn
Hu Xiaoxiang, Associate Professor, Northwest Polytechnic University
E-mail: xxhu@nwpu.edu.cn
Download: Special Session #12.pdf
Introduction of Special Session #12
With the rapid development of the aircraft, especially unmanned aerial vehicles, and the progress of flight control technology, aircraft is playing an increasingly important role in both the military and civil fields. In order to ensure the reliability of the aircraft mission, it is necessary to study the fault diagnosis and fault-tolerant control methods of the aircraft and solve the critical problems in the actual scenes. The special session focus on aircraft fault diagnosis methods and fault-tolerant control technologies based on models, data, knowledge or artificial intelligence, aircraft health management, and test platform and prototype development related to aircraft. The purpose of this session is to provide an exchange platform for relevant scientific researchers in the field of aircraft fault diagnosis and flight control, report the new relevant research results in recent years, and boost the technological development in this field.
Special Session #13
Underwater robot condition monitoring and fault prediction
Session Organizers:
Associate Prof. Chaoqun Duan, Shanghai University, China,
Email: chaoqun@shu.edu.cn
Dr. Xin Li, Nanjing Institute of Technology, China
Email: lixin1990@nuaa.edu.cn
Download: Special Session #13.pdf
Introduction of Special Session #13
Underwater robots are increasingly vital in various fields such as deep-sea detection, marine environmental monitoring, and ocean resource exploration. However, due to the complexity of the marine environment and the rigorous demands on underwater robots, they are prone to malfunctions during operations in the ocean. This not only poses risks and inconvenience to marine operations but also affects the reliability and work efficiency of the robots. Therefore, achieving status monitoring and fault prediction for underwater robots is crucial. Through multi-parameter monitoring and status evaluation of underwater robots, it is possible to promptly detect abnormal behavior, predict robot malfunctions, and carry out maintenance and repairs in advance, which can reduce failure rates and maintenance costs and improve the reliability and work efficiency of the robots. Thus, this special conference aims to explore techniques of underwater robot status monitoring and fault prediction for improving the reliability performance of underwater robots.
