Download: Call for Special Sessions

 

ICSMD 2022 is soliciting special session proposals. Prospective organizers are invited to submit their proposals to the Special Sessions Co-Chairs, Jinxing Liang (Southeast University, j-liang@seu.edu.cn), and/or Zhi Tao (Soochow University, taoz@suda.edu.cn), by June 20th, 2022. The special session proposal should include the title of the special session, a short presentation and motivation of the significance of the special session topic. 


The special sessions are intended to stimulate in-depth discussions in special areas relevant to the conference theme. Each proposal will be carefully evaluated, and the accepted special sessions will be announced on the conference website. 


The organizers are welcome to promote their special sessions through various venues, and will coordinate the review process for their session papers. The conference proceedings will include all papers from the special sessions.

 

Special Session #01  Advanced Sensing, Monitoring and Diagnosis in Power Transmission and Storage

Special Session #02  Advanced Measurement and Intelligent Processing in Aerospace

Special Session #03  Intelligent Test, Diagnosis and Evaluation of High Confidence Equipment

Special Session #04  Non-contact Measurement and Monitoring for Turbomachiner

Special Session #05  AI-Enabled Multimodal Data Analytics for Prognostics and Health Management of ndustrial Equipment

Special Session #06  Imbalanced Learning Methods for Intelligent Fault Diagnosis of Mechanical Systems

Special Session #07  Quartz MEMS Technology and Artificial Intelligence Methods

Special Session #08  Data-driven Compound Fault Diagnosis of Bearings

Special Session #09  Advanced Vibration Measurement and Analysis for Key Equipment and Structures

Special Session #10  Intelligent Sensing and Edge Computing for Machinery Fault Diagnosis

Special Session #11  NDT&E and Intelligent Monitoring

Special Session #12  Remaining Useful Life (RUL) Prediction of Rotating Machinery

Special Session #13  Inspection Robotics and Explainable AI

 

Special Session #1
Advanced Sensing, Monitoring and Diagnosis in Power Transmission and Storage

Session Organizers:

 

Download: Special Session #1.pdf

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The fast growth of the renewable electricity results in new challenges in electrical grids. Any failures of the power equipment might lead to a serious blackout. Therefore, there is an urgent demand to develop advanced and convenient condition monitoring methods to ensure the safety of electrical grids.

In particular, the development of advanced power equipment and smart sensing technology is urgent to make breakthroughs in sensing principles, materials, devices, network transmission, data processing, and comprehensive applications.

In this Session, we aim to provide a forum for colleagues to report the most up-to-date research in condition diagnostic of power apparatuses in power transmission and storage, as well as comprehensive surveys of the state-of-the-art in relevant specific areas. Both original contributions with theoretical novelty and practical solutions for addressing particular problems are solicited.

 

The topics of interest include, but are not limited to:

  • Advanced sensing principles and method for condition monitoring of power apparatuses
  • Novel diagnosis techniques and measurement systems
  • Advanced sensors (optical fiber, MEMS, etc.) design and testing
  • Distributed sensing methods in power transmission and storage
  • Data acquisition and processing algorithms for diagnosis
  • Application cases of condition monitoring in power transmission and storage
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  • Special Session #2

Advanced Measurement and Intelligent Processing in Aerospace

Session Organizers:

 

Download: Special Session #2.pdf

 

With the development of science and technology, mankind has a deeper understanding and exploration of aerospace. At present, space exploration and application have attracted more and more attention in the front edge of science and technology. Due to the particularity of space application, how to use limited resources to improve the accuracy of aerospace measurement and enhance the on orbit intelligent processing ability is an important means to improve the application scope of spacecraft and promote the long-term reliable operation of spacecraft. This topic aims to focus on the new development of aerospace measurement and on orbit intelligent processing technology.

 

The topics of interest include, but are not limited to:

Advanced spacecraft measurement technology

  • Remote sensing image processing and intelligent computing technology
  • Aerospace environment detection and space radiation protection technology
  • Design of spacecraft systems and key components
  • Spacecraft automatic test and intelligent health monitoring
  • Key technologies in spacecraft applications

  • Special Session #3

    Intelligent Test, Diagnosis and Evaluation of High Confidence Equipment

    Session Organizers:
  • Jingli YANG, Harbin Institute of Technology
  • Miao WANG, Shanghai Jiao Tong University
  • Yunlong SHENG, Shandong University of Technology

  • Download: Special Session #3.pdf

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  • With the development of science and technology, high confidence equipment (e.g. missiles, aircraft, satellites) are applied in all area such as national defense, industry, even in the daily lives, where failures could result in loss of life, significant property damage, or damage to environment. Test, diagnosis and evaluation techniques are vital tools to enhance the long-term reliability of high confidence equipment, which can reduce or even avoid catastrophic accidents. This session is intended to focus on the new development of intelligent test, diagnosis and evaluation techniques for high confidence equipment.

     

    The topics of interest include, but are not limited to:

  • Cutting-edge test techniques for improving reliability of high confidence equipment
  • Intelligence diagnosis of high confidence equipment or its devices
  • Assessment of artificial intelligence algorithms used in high confidence equipment
  • Evaluation methods and systems of high confidence equipment

  • Special Session #4

    Non-contact Measurement and Monitoring for Turbomachiner

    Session Organizers:
  • Baijie QIAO, Xi’an Jiaotong University
  • Yanan WANG, Xi’an Jiaotong University
  • Bi WEN, Science and Technology on Altitude Simulation Laboratory
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  • ​Download: Special Session #4.pdf

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  • The measurement and monitoring of turbomachinery such as aero-engine, gas turbine, steam turbine and compressor are quite essential, since the working temperature, rotating speed and pressure ratio is greatly increasing. The traditional contact measurements are to use strain gauge or accelerometer to measure the vibration of turbomachinery. However, such contact measurements cannot be used for long-term health monitoring. The non-contact, non-intrusive measurements such as blade tip timing, microphone array, laser Doppler vibrometer and DIC are emerging and bring great promise to monitor turbomachinery.

     

    The topics of interest include, but are not limited to:

  • Blade tip timing for vibration parameter identification, synchronous and asynchronous vibration identification, etc
  • Acoustic signal-based fault diagnosis including gas path faults and mechanical vibration faults
  • Blade/disk/blisk health monitoring
  • Non-contact measurement with uncertainty quantification

  • Special Session #5

    AI-Enabled Multimodal Data Analytics for Prognostics and Health Management of

  • Industrial Equipment

     

    Session Organizers:
  • Dr. Ruyi HUANG, South China University of Technology
  • Prof. Shibin WANG, Xi’an Jiaotong University
  • Dr. Ke FENG, University of British Columbia

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    Powerfully driven by advanced sensing, measurement, and analytics technologies, the manufacturing industry has embraced the new era of the fourth industrial revolution (Industry 4.0).  The manufacturing industry factory is a typical multimodal data environment, which may not only provide more useful information but also bring great opportunities and challenges for prognostics and health management (PHM) of industrial equipment. Recent advances in the theory and methodology for multimodel learning have provided promising tools for dealing with the challenges encountered in practical diagnostics, prognostics, and health management of industrial equipment.

     

    Multimodal learning is inspired by the phenomenon that human beings have an innate and remarkable capability to perform multimodal pattern recognition despite noisy sensory signals and conflicting inputs. However, such an “easy” task is difficult for the majority of PHM methods as so far. Therefore, this special session aims at seeking papers on recent research, development, applications and comprehensive surveys that focus on AI-enabled multimodal data analytics for diagnostics, prognostics and health management of industrial equipment.

     

    The topics of interest include, but are not limited to:

  • Advanced multimodal sensing and measurement in manufacturing industry
  • Multimodal data processing and representation in manufacturing industry
  • Multimodal data fusion methodology for PHM of industrial equipment
  • Multimodal or cross-modal feature engineering for PHM of industrial equipment
  • Heterogeneous transfer learning between multimodal sensors for PHM of industrial equipment
  • Novel benchmark multimodal industrial datasets
  • Novel applications with multimodal industrial datasets

  • Special Session #6

    Imbalanced Learning Methods for Intelligent Fault Diagnosis of Mechanical Systems

    Session Organizers:
  • Prof. Haidong SHAO, Hunan University
  • Dr. Te HAN, Tsinghua University
  • Prof. Long WEN, China University of Geosciences

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  • Download: Special Session #6.pdf

     

  • Intelligent fault diagnosis of mechanical systems is becoming more significant in modern industrial or manufacturing applications. The failures of critical machine components may lead to serious economic loss, and even major accidents. As machine learning grows more and more advanced, the fault diagnosis techniques are becoming more and more intelligent. The problem of imbalanced learning has always been a hot topic in this field, as the collected data from industrial machines are often imbalanced. Tackling the issues raised by imbalanced domains is crucial to learn the useful fault features and establish a highly accurate fault diagnosis model. This special session is interested in articles on the latest research progress and achievements of imbalanced learning for intelligent fault diagnosis of mechanical systems.

     

    The topics of interest include, but are not limited to:

  • Imbalanced learning in mechanical big data
  • Oversampling methods for extremely imbalanced mechanical data flows
  • Machine fault feature selection and transformation in imbalanced domains
  • Deep Learning in imbalanced machinery fault diagnosis
  • Cost-sensitive learning in imbalanced machinery fault diagnosis
  • Ensemble Learning in imbalanced machinery fault diagnosis
  • Graph neural networks in imbalanced machinery fault diagnosis

  • Special Session #7

    Quartz MEMS Technology and Artificial Intelligence Methods

    Session Organizers:
  • Jing Ji, Xidian University
  • Qieshi Zhang, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
  • Meng Zhao, Xidian University

  • Download: Special Session #7.pdf

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  • Using the quartz MEMS process, the miniaturization of Quartz devices is realized with high Q-factor, good S/N ratio and product uniformity. Quartz MEMS devices such as: Crystal Oscillator, QCM, high sensitivity sensors, are now widely used in various areas. Statistics indicated that in last 10 years, the sales volume of quartz crystal devices increased two times and still growing. There is also an increasing need for quartz MEMS technology to approach the challenge of Si MEMS and other new materials technology. Artificial intelligence methods are widely used in several areas, such as data analysis of MEMS devices, signal process and feature extraction, Navigation systems application, Target trajectory prediction and understanding, and other applications. This topic aims to show and share new ideas and achievements of the new development and application of Quartz MEMS devices, artificial intelligence related technologies and applications with relevant experts, scholars and engineers around the world.

     

    The topics of interest include, but are not limited to:

  • Design and optimization of quartz MEMS devices
  • Manufacture process of quartz MEMS devices
  • Etching simulation of anisotropic material
  • Sensors based on quartz crystal: gyro sensors, temperature sensors, pressure sensors, accelerometer, etc
  • Advanced signal process methods for feature extraction
  • Intelligent fault diagnosis based on deep/transfer learning and big data
  • Navigation systems: INS, GNSS, Visual navigation, Celestial navigation system, UWB
  • Target trajectory prediction and understanding
  • artificial intelligence and its application, etc

  • Special Session #8

    Data-driven Compound Fault Diagnosis of Bearings

    Session Organizers:
  • Weiguo HUANG, Soochow University
  • Juan XU, Hefei University of Technology
  • Xu DING, Hefei University of Technology

  • Bearing is an indispensable basic component in industrial equipment, and bearing fault diagnosis is important to ensure the safety of equipment and personnel. Nowadays, the rapid development of modern industrial equipment has led to complex and diversified types of bearing faults, resulting in the compounding of many different faults simultaneously. Notably, the compound faults in bearings are not a simple linear superposition of single faults, and their coupling and complexity leads to a significant increased difficulty in fault diagnosis. Although numerous data-driven methods have emerged as powerful tools for compound fault diagnosis, it remains a challenge to use these advanced methods and techniques effectively while considering real industrial scenarios.

     

    This session aims at seeking papers on recent research, development, applications and comprehensive surveys that focus on data-driven compound fault diagnosis of bearings.

     

    The topics of interest include, but are not limited to:

  • Dynamic characteristics analysis of compound fault
  • Signal processing-based compound fault diagnosis
  • Machine learning-based compound fault diagnosis
  • Deep learning-based compound fault diagnosis
  • Transfer learning-based compound fault diagnosis

  • Special Session #9

    Advanced Vibration Measurement and Analysis for Key Equipment and Structures

    Session Organizers:
  • Yuyong XIONG, Shanghai Jiao Tong University
  • Changqing SHEN, Soochow University
  • Dong WANG, Shanghai Jiao Tong University

  • Vibration phenomena are wide spreads from the natural world to the engineering. Vibration measurement science and analysis technology play an important role in the design, manufacture and service of key equipment and structures, which is highly desired for high-end equipment manufacturing. Developments of advanced vibration measurement methods and instruments with novel sensing principles, noncontact manners, high performance, and extreme environmental adaptability are of great interest for a wide range of fields. Meanwhile, effective vibration signal analysis methods are critical to perform data or feature-driven monitoring, diagnosis, control and management, offering intelligent vibration-based solutions. This special session aims to provide a platform to report advances in methods, systems and applications of various vibration measurement and analysis technology.

     

    The topics of interest include, but are not limited to:

  • Vibration measurement sensors
  • Vibration measurement systems: architectures, methods and interfaces
  • Microwave/Terahertz-based vibration sensing
  • Vision/laser-based vibration sensing
  • Non-intrusive monitoring
  • Signal processing algorithms
  • Machine learning and big data analysis
  • Feature extraction and system identification
  • Time-frequency analysis
  • Condition monitoring and fault diagnosis
  • Intelligent vibration measurement and analysis
  • Vibration measurement and analysis applications

  • Special Session #10

    Intelligent Sensing and Edge Computing for Machinery Fault Diagnosis

    Session Organizers:
  • Prof. Qingbo He, Shanghai Jiao Tong University
  • Asso. Prof. Siliang Lu, Anhui University

  •  

  • Download: Special Session #10.pdf

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  • Machinery fault diagnosis is generally realized by analyzing the sensor signals, and the emerging intelligent sensing technologies improve the accuracy and efficiency of fault diagnosis. Edge computing is a paradigm that offloads the computations and analytics workloads onto the edge devices to accelerate the computation efficiency, reduce the channel occupation of signal transmission, and reduce the storage and computation workloads on the cloud servers. The combination of the intelligent sensing and edge computing shows great potentials in machine real-time signal processing, low-latency fault diagnosis, and high-efficient predictive maintenances. This special session aims to provide a platform to report the advances in methods, systems and applications of intelligent sensing and edge computing for machinery fault diagnosis.

     

    The topics of interest include, but are not limited to:

  • Intelligent sensing
  • Edge computing
  • Edge intelligence, collaboration of cloud and edge computing
  • Sensing and sensor technology, instruments and instrumentation
  • Federated learning, block chain, and distributed computing
  • Platform and algorithm for embedded systems
  • Internet of things and wireless sensor networks
  • Energy harvesting and management for sensors
  • Acquisition, compression, and transmission of the conditioning signals
  • Real-time fault diagnosis and high-efficient predictive maintenances

  • Special Session #11

    NDT&E and Intelligent Monitoring

    Session Organizers:
  • Prof. Liuyang Zhang, School of Mechanical Engineering, Xi’an Jiaotong University
  • Asso.Prof. Yu Sun, School of Mechanical Engineering, Xi’an Jiaotong University

  •  

  • Download: Special Session #11.pdf

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  • Non-Destructive Testing and Evaluation (NDT&E) plays a critical part in manufacture and maintenance of mechanical equipment for cost reduction as well as safety insurance. Advanced NDT&E methods aim at saving time and cost, improving range and accuracy, hence the significance in both research and application. To achieve this goal, not only testing instruments themselves but also signal processing algorithms deserve attention. This topic will mainly focus on the progress in advanced inspection systems for detecting, identifying, and locating structural defects in both metallic and non-metallic materials. The emphasis is on increasing the performance of inspection systems to provide damage mode and quantitative characterization of the material state. Presentations addressing the following topics and considering any NDT&E methods will be welcome.

     

    The topics of interest include, but are not limited to:

  • NDT&E technologies such as ultrasound, thermography，magnetic-optic imaging testing and x-ray computed tomography
  • Novel NDT&E technologies such as terahertz, wavefield imaging and nonlinear ultrasound
  • Computational and analytic models for different NDT&E technologies
  • Machine learning and optimization methods used in NDT&E
  • Mobile NDT&E systems for maintenance, repair and overhaul used in robotics
  • Advanced methods of applying and interrogating sensors for structural health monitoring
  • Metal surface coating or multilayer coating testing methods
  • Quantization and evaluation algorithm about NDT&E
  • Fusion methods and algorithms about NDT&E
  • Physical mechanism and sensors about NDT&E
  • Advanced signal processing and computational imaging algorithms for time saving and accuracy improvement
  • Prognostics and health management

  • Special Session #12

    Remaining Useful Life (RUL) Prediction of Rotating Machinery

    Session Organizers:
  • Prof. Huaqing Wang, Beijing University of Chemical Technology
  • Asso.Prof. Liuyang Song, Beijing University of Chemical Technology

  •  

  • Download: Special Session #12.pdf

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  • The remaining useful life (RUL) prediction has attracted substantial attention recently due to its importance for the Prognostic and Health Management. This special issue of Remaining Useful Life (RUL) Prediction of Rotating Machinery aims at providing a platform for experts and scholars to discuss advanced technology and applications of Remaining Useful Life (RUL) Prediction in Rotating Machinery. Submissions related to the advanced and emerging technologies and their applications in Remaining Useful Life (RUL) Prediction studies are encouraged. 

     

    The topics of interest include, but are not limited to:

  • Machined learning-based approaches for remaining useful life prediction
  • Transfer learning-based approaches for remaining useful life prediction
  • Multiple data-driven approaches for remaining useful life prediction
  • The health indicator (HI) construction approaches
  • The first predicting time (FPT) selection approaches
  • RUL prediction approaches for bearings and other rolling elements
  • Applications of RUL prediction in railway Systems, aerospace Systems and wind energy system
  • Real-time remaining useful life prediction technology
  • Other advanced technologies for RUL prediction

  • Special Session #13

    Inspection Robotics and Explainable AI

    Session Organizers:
  • Prof. Ruqiang Yan, School of Mechanical Engineering, Xi’an Jiaotong University
  • Dr. Laihao Yang, School of Mechanical Engineering, Xi’an Jiaotong University 
  • Prof. Xin Dong, Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham
  • Email: Xin.Dong@nottingham.ac.uk

    •  

    • Download: Special Session #13.pdf

    •  

    • Intelligent inspection is of great significance for the maintenance and safety insurance of mechanical equipments in the field of aero-engine, aircraft, and nuclear power. However, the harsh inspection environment, long-narrow space, and scarce label data make it very challenging for the intelligent, efficient, and accurate inspection of these equipments. Under this context, the concept of “robotics + autonomous intelligence” emerges in the field of inspection and maintenance for mechanical equipment, and attracts extensive attention from OEMs, end-users, and academics. The new paradigm of inspection and maintenance will promote the robotics, sensors, and detection technology to an upper level due to the high demand on reachability in crammed space, multi-mode environment sensing, pattern identification and classification accuracy. This topic mainly addresses the emerging theory and technology in the field of intelligent robotics-based in-situ inspection for high-end mechanical equipments, and focuses on the intelligent robotics, multi-mode sensing, and explainable machine learning, which is intended for the intelligent, efficient, and accurate inspection. 

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    The topics of interest include, but are not limited to:

  • Latest progress of state-of-the-art technologies in the field of robotics, sensing, and pattern identification for intelligent inspection
  • Emerging robotics technologies such as continuum robotics, soft robotics, micro-crawling robotics, and bio-inspired robotics
  • Intelligent modeling, control and path planning for in-situ inspection
  • Intelligent sensing technologies such as flexible sensing, multi-mode sensing integration, and damage imaging method
  • Deep learning methods in vision, vibration, and ultrasonic -based damage detection
  • Explainable machine learning in the field of intelligent structure design, modeling, control, and detection

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Registration Paper Submission

Important Dates

15th August 2022 25th September 2022 - Manuscript Submission
15th October 2022 - Acceptance Notification
1st November 2022 - Camera Ready Submission
1st November 2022 10th November 2022 - Early Bird Registration

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