Special Session 01

Special Session 01: Intelligent Condition Monitoring,

System Control & Data analytics for Linear Drives and MAGLEV Systems

Session Organizers: 
• Yougang Sun, Tongji University, Email: 1989yoga@tongji.edu.cn
• Junqi Xu, Tongji University, E-mail: xujunqi@tongji.edu.cn
• Weihua Ma, Southwest Jiaotong University, E-mail: mwh@swjtu.edu.cn

Linear Drives and MAGLEV (magnetic levitation) technologies are widely used in maglev trains (such as Shanghai line and Changsha line), precision machine tools, high-speed bearings, series hybrid electric car generators small compressors, etc. Considering the characteristics of linear motor and maglev system, the design, control and monitoring of linear drives and MAGLEV systems are faced with challenges such as strong nonlinearity, the end-effects, electromagnetic interference in monitoring environment and time-varying system parameters. In order to overcome the above difficulties, a large number of monitoring data can be collected for data analysis and control optimization of linear drives and MAGLEV system. Nowadays, intelligent sensing, measurement and data analysis methods are proposed to make machine monitoring develop towards automation and intelligence. This special session is aimed to provide a high-quality academic exchange platform for the most recent advances in the areas related to intelligent condition monitoring, system control & data analytics for linear drives and MAGLEV Systems. This special session will reflect the state of the art of linear drives and MAGLEV systems in science and engineering practice and is an opportunity to exchange scientific, technical and experimental ideas in a multi-disciplinary and inter-disciplinary circumstance. 

The potential topics of this special session include, but are not limited to:

● Application of smart sensor, intelligent health monitoring technology, or Advanced signal pre-processing methods for Linear Drives & MAGLEV systems;
● Modeling of linear motor & MAGLEV system based on data driven methods;
● Deep learning based approaches for damage detection or condition assessment for Linear Drives & MAGLEV systems;
● Neural network-based control, model predictive control & control system optimization for Linear Drives & MAGLEV systems;
● Predictive maintenance decision making: MAGLEV trains, Linear motor, Magnetic bearing, Flywheel energy storage system, etc.;
● Advanced machine learning for data analytics of Linear Drives & MAGLEV systems;
● Other related topics of linear Drives & MAGLEV systems.

 Special Session 02

Special Session 02: NDT&E and Intelligent Inspection System

Session Organizer: 
•  Liuyang Zhang, Professor, School of Mechanical Engineering, Xi’an Jiaotong University, China  
Email: liuyangzhang@xjtu.edu.cn

Non-Destructive Testing and Evaluation (NDT&E) plays an important role in the proper operation and maintenance of mechanical equipment. Appropriate use of NDT&E helps to solve the problems in the production and avoids catastrophic equipment failures and unsafe working conditions. Currently technological achievements of NDT&E have been widely used in transportation, aerospace, automobile, manufacturing, petrochemical, national defense industry and many other fields , making NDT&E becomes the guarantee of modern industrial safety. Nondestructive testing focuses on the latest research and breakthrough in NDT&E and structural health monitoring. The emphasis must be the development of advanced inspection system aiming at accurate detection of critical defects in metal and non-metallic materials and structures of mechanical equipment. In addition, nondestructive testing devotes to combining technology development with industrial applications to promote the development of structural health monitoring. To communicate latest R&D achievements to academic and industrial applicants on the one hand and to discuss advanced and improved methods both with scientists as well as industrial researchers on the other, presentations addressing the following topics and considering any NDT&E methods will be welcome.

●The development of original research and development in all categories of the fields of NDT&E such as ultrasonics, electromagnetics, radiography, optical and thermal methods；
● Novel NDT&E technologies such as terahertz, wavefield imaging and nonlinear ultrasound;
● NDE sensors and their applications both for inspection and process control;
● New NDE systems for structural health monitoring;
● Computational and analytic models to NDT&E problems;
● Advanced analytic methods to interpret large complex data sets;
● Data evaluation and signal processing with a scope on big data management;
●  Mobile NDT&E for maintenance, repair and overhaul including robotics;
●  Innovative applications of established methods; 
●  Prognostics and Health Management;

 Special Session 03

Special Session 03: Advanced Sensing, Monitoring and Diagnosis in Smart Grid

Session Organizers: 
•  Dr. Yu Chen, Xi’an Jiaotong University, E-mail: chenyu@xjtu.edu.cn
•  Dr. Zhe Li, Shanghai Jiaotong University, E-mail: zhe_li@sjtu.edu.cn

With the deepening of clean and low-carbon transformation of energy, large scale development and utilization of renewable energy, distributed energy and energy storage are developing rapidly. Digital technology is used to empower the traditional power grid, continuously improve the intellisense ability, interaction level and operation efficiency of the power grid, effectively support various energy access and comprehensive utilization, and continuously improve energy efficiency. On the basis of the research of new sensing, monitoring and diagnosis technology, the modern information technology and advanced communication technology, such as mobile Internet and artificial intelligence, are fully applied to realize the comprehensive condition intellisense, efficient information processing, convenient and flexible application for key equipment in smart grid, such as transformers, switches, cables, power transmission lines, wind turbines, solar photovoltaic systems. This session is intended to focus on the advanced sensing, monitoring and diagnosis in smart grid.

Research interests include but are not limited to:

● Advanced sensing and its application for power equipment and renewable energy conversion system
● Conditioning monitoring and diagnosis for transformer, GIS, cable, power transmission line, wind turbine, photovoltaic system, etc.
● Application of image processing in the conditioning monitoring and diagnosis field
● Emerging new applications with 5G mobile internet and IoT in electricity
● Related research on edge computing, artificial intelligence and big data in smart grid

 Special Session 04

Special Session 04: Weak Signal Processing and Incipient Fault Detection

Session Organizers: 
•  Yi Qin, Chongqing University, China, E-mail: qy_808@cqu.edu.cn
•  Yi Wang, Chongqing University, China, E-mail: wyyc@cqu.edu.cn

Extracting of weak fault features is very important in many rotating machineries, such as aircraft engines, wind turbines, high-speed trains, etc. However, fault induced transient impulsive features may seriously contaminated by background noise, adjacent component vibrations and nonstationary operating conditions. In recent years, many signal enhancement, source separation and nonstationary signal processing methods have been proposed. However, adaptively extraction of weak fault features for incipient fault detection is still a challenge. In order to facilitate the development of weak signal processing and incipient fault detection for a variety of mechanical and electrical systems, Prof. Yi Qin and Dr. Yi Wang from Chongqing University will organize a special section titled as “Weak signal processing and incipient fault detection” in International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD2021). 

This special session focuses on the following topics (but not limit to):

● Weak signal enhancement and source separation
● Deep neural networks and its application to weak signal detection
● Advanced time-frequency analysis algorithm
● Health indicator construction for incipient fault detection
● Instantaneous angular speed estimation techniques
● Nonstationary mechanical signal processing methods
● Other advances in weak signal processing and incipient fault detection

Scholars focusing on mechanical weak signal processing and incipient fault detection are welcome to submitting your papers to this special session.

 Special Session 05

Special Session 05: Advanced Control, Navigation and Signal Processing

Methods for Multiple Autonomous Unmanned Systems

Session Organizers: 
•  Dr. Haoqian Huang, Hohai University, Email: hqhuang@hhu.edu.cn
•  Dr. Huiliao Yang, Hohai University, Email: huiliao.yang@hhu.edu.cn
•  Dr. Yuan Yang, Southeast University, Email: yangyuan@seu.edu.cn

As a technology that can replace humans in performing dangerous tasks, automatic unmanned systems have broad application prospects in military and civilian fields. Advanced automatic unmanned systems have been applied successfully to submarine pipeline laying and exploration, marine mineral resource exploration, marine biological resource survey, submarine reconnaissance, and submarine training. Multi-autonomous unmanned systems focus on the latest research and breakthroughs in advanced control, navigation and signal processing methods. Among them, high-precision navigation positioning information is a prerequisite for safe and effective implementation of underwater automatic unmanned system tasks. At present, the mainstream navigation systems mainly include inertial navigation, satellite navigation, model prediction navigation, geophysical navigation, visual navigation, and acoustic navigation. This special issue aims to provide advanced control, navigation and signal processing methods for multiple autonomous unmanned systems.
Research interests include but are not limited to:
● New investigation methods and sensors for path planning;
● Compensation and calibration algorithms for navigation sensors;
● Advanced sensors and information fusion for underwater navigation;
● Underwater image enhancement and 3D image reconstruction;
● Mobile robot navigation and control based on intelligent learning / bionics；
● Cooperative control and navigation in multi-unmanned systems;
● Quantum device and intelligent measurement

 Special Session 06

Special Session 06: Intelligent Process Monitoring and Optimization

for Advanced Manufacturing Systems 

Session organizer:
• Min Xia, Lancaster University, UK; Email: m.xia3@lancaster.ac.uk
• Siliang Lu, Anhui University, China; Email: silianglu@ahu.edu.cn
• Haidong Shao, Hunan University, China; Email: hdshao@hnu.edu.cn

Advanced manufacturing is the use of innovative technology to improve products or processes.  Advanced manufacturing systems such as industrial robots, laser cutting, metal additive manufacturing, and CNC machining, have become increasingly complex and dynamic. To guarantee the quality, efficiency, and safety of the manufacturing, process monitoring and optimization of these sophisticated systems are crucial. However, due to the complexity of the manufacturing process, effective process monitoring and optimization are still challenging. Recent developments in advanced sensing technologies and the new generation of data analytics technologies provide new opportunities. The efforts in both academia and industry on sensing and data analytics for advanced manufacturing systems are towards the directions of in-situ, distributed, real-time, intelligent, and autonomous. This special session focuses on consolidating research efforts that aim at innovative sensing and data analytics for process monitoring and optimization of advanced manufacturing systems. This special session encourages submissions that cover the following topics, but are not limited to:
● Condition monitoring of key equipment in advanced manufacturing systems
● Advanced sensing solution for extreme working conditions
● Multimodal sensor fusion and data analysis
● Real-time process monitoring for metal additive manufacturing
● Process optimization and control of advanced manufacturing processes
● Digital twin-assisted process monitoring and optimization 
● Prognostics and health management (PHM) applications within the advanced manufacturing 

 Special Session 07

Special Session 07: Data-driven Engineering Practices in The Fields of Energy, Environment, and Biomedicine

Session Organizers:

• Hui Xiao, Tongji University, Email: xiaohui@tongji.edu.cn

• Xuefeng Li, Tongji University, Email: lixuefeng@tongji.edu.cn

With the generation of massive data and the improvement of computing power, intelligent sensing and detection technologies around information processing, data mining, pattern recognition, and information fusion have been widely used in energy, environment, and biomedicine. For example, in the energy field, we can understand, model, and predict the impact of physical behavior and human activities on energy through real-time data collection and analysis; in the environment field, we can use big data for risk assessment and analysis to realize the monitoring of natural and artificial environments and emergency management, which can improve the level of construction and management of smart buildings, smart communities, and smart cities; in the biomedicine field, we can process a large amount of medical data to mine potential information, thus to achieve auxiliary treatment, health management, and clinical decision assistance, and provide the methodological support for disease diagnosis and treatment.

The potential topics of this special session include, but are not limited to:

● Application of information processing technologies such as information collection, noise control, high-speed transmission, and cloud-edge storage.

● Application of data mining technologies such as machine learning methods, statistical methods, neural network methods, and database methods.

● Application of pattern recognition technologies such as neural network, support vector machine, decision tree, and clustering.

● Application of multi-source heterogeneous sensing data fusion and processing technology.

● Engineering practices of computer vision technology in the fields of energy, environment, and biomedicine.

● Engineering practices of prediction theory and prediction model in energy, environment, and biomedicine.

● Engineering practices of intelligent computing in the fields of energy, environment, and biomedicine