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• Session Organizers:

• Dr. Laihao Yang, School of Mechanical Engineering, Xi’an Jiaotong University, China  

  Email: yanglaihao@xjtu.edu.cn

  Dr. Jun Dai, School of Mechatronic Engineering, Beijing Institute of Technology, China

  Email: daijun@bit.edu.cn

  Dr. Xin Zhang, School of Mechanical Engineering, Southwest Jiaotong University, China

  Email: xylon.zhang@swjtu.edu.cn

  Dr. Yu Sun, School of Mechanical Engineering, Xi’an Jiaotong University, China  

  Email: yu.sun@xjtu.edu.cn

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• Download: Special Session #15.pdf
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• Intelligent inspection and maintenance are of great significance for the 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 and maintenance of these equipments. Under this context, the concept of “robotics + autonomous intelligence” emerges in the field of inspection and maintenance for mechanical equipments, and attracts extensive attention from OEMs, end-users, and academics. The new paradigm of inspection and maintenance will promote 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 robotized in-situ inspection and maintenance for high-end mechanical equipments, and focuses on intelligent robotics, multi-mode sensing, and explainable machine learning, which is intended for intelligent, efficient, and accurate inspection and maintenance.

   

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

• • Latest progress of state-of-the-art technologies in the field of robotics, sensing, control, and pattern identification for intelligent inspection and maintenance
• • Emerging robotics technologies such as soft robotics, micro-crawling robotics, and bio-inspired robotics for intelligent inspection and maintenance
• • Embedded intelligence for robotics
• • Intelligent fault diagnosis and health monitoring of the key components (gear, bearing, etc.) in industrial robotics
• • Intelligent control and path planning for in-situ inspection and maintenance
• • Intelligent sensing technologies such as flexible sensing, multi-mode sensing integration, and damage imaging method
• • Deep learning methods in vision-based damage detection
• • Explainable machine learning in the field of intelligent structure design, control, and detection