Advanced Techniques for  Fault Detection based on Perception Sensors

Submission Guideline:

Please submit your manuscript via Online Submission System: https://easychair.org/conferences/?conf=rcae2024
Please choose Special Session: Advanced Techniques for  Fault Detection based on Perception Sensors

Introduction:

The reliability and efficiency of industrial machinery are critical for maintaining optimal operational performance and minimizing downtime. Traditional fault detection methods, while effective, often lack the precision and adaptability needed for modern, complex systems. With advancements in perception sensor technology, such as acoustic emission, laser, camera, and vibration sensors, there is a significant opportunity to enhance fault detection capabilities. These sensors provide real-time data and insights, enabling early detection and diagnosis of potential issues before they escalate into critical failures.
This special session aims to explore the latest developments and applications of perceptual sensors in machinery fault detection. We invite researchers and industry experts to share their insights, methodologies, and case studies that demonstrate the integration of these advanced sensors into fault detection systems. The session will cover a wide range of topics, including but not limited to sensor data processing, machine learning algorithms for fault detection, and practical implementations in various industries. By fostering discussions and collaborations, we hope to push the boundaries of current fault detection practices and pave the way for more reliable and efficient industrial operations.

To ensure a successful and well-attended special session, we plan to implement the following promotional strategies:

1. Call for Papers: Distribute a detailed call for papers through academic and industry networks, targeting researchers and professionals working in the field of sensor technology and fault detection.

2. Industry Collaboration: Engage with leading companies and research institutions to encourage submissions and participation, highlighting the relevance and potential impact of the session’s topics.

3. Industry Collaboration: Engage with leading companies and research institutions to encourage submissions and participation, highlighting the relevance and potential impact of the session’s topics.

4. Extended Presentations: Consider allowing extended presentation slots for particularly significant contributions or industrial talks that provide in-depth analysis and practical applications.

5. Online and Social Media Campaigns: Utilize online platforms and social media channels to promote the session, reaching a wider audience and encouraging engagement and participation.

We anticipate having 5-8 papers presented during the session, with potential for special talks that do not require an accompanying paper or that may be allocated double-time slots for more comprehensive presentations.

Organizers:

Yujie Zhang, Associate Professor

Sichuan University, China

Yujie Zhang received the B.E. degree from Harbin Institute of Technology at Weihai, Weihai, China, in 2014, and the Ph.D. degree from Harbin Institute of Technology (HIT), Harbin, China, in 2021. He is currently an associate professor with the College of Electrical Engineering, Sichuan University, Chengdu, China. Dr. Yujie Zhang has led several research projects, including the National Natural Science Foundation of China, Natural Science Foundation of Sichuan Province, China Postdoctoral Science Foundation, etc. Additionally, Dr. Yujie Zhang has collaborated with various institutes and enterprises on research topics. To date, Dr. Yujie Zhang has published over 10 SCI/EI indexed journal papers, applied for and been granted 7 national invention patents, and serves as a reviewer for various journals, including IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal, Reliability Engineering & System Safety, ISA Transactions, Neural Computing and Applications, and Chinese Journal of Electronics. His current research interests include prognostics and health management, condition monitoring, data-driven degradation modeling and electronic-mechanical system simulation.

Ming Li, Professor

Anhui Polytechnic University, China

Professor Ming Li at the School of Electrical Engineering, Anhui University of Engineering. He is also a doctoral supervisor at Southwest Forestry University. His main research interests is acoustic emission nondestructive testing. In this field, he has received funding from three National Natural Science Foundation projects. The main research content is to use acoustic emission detection method to monitoring the health status of wood and timber components and to reveal its damage and fracture mechanism. From 2021, he began on focus on the application of acoustic emission technology in the SHM of industrial robots.

Li Guo, Professor

Anhui Polytechnic University, China

Professor Li Guo at the School of Electrical Engineering, Anhui University of Engineering. She has collaborated with various institutes and enterprises on research topics. To date, She has published over 30 SCI/EI indexed journal papers, applied for and been granted 6 national invention patents, and serves as a reviewer for various journals, including IEEE Transactions on Neural Network and Learning Systems, IEEE Transactions on Industrial Informations, Neurocomputing,  IEEE Transactions on Artificial Intelligence and Journal of Control and Decision. Her main research interests is data-driven fault diagnosis and and health management. In this field, she has received National Natural Science Foundation projects.