Machine Vision-Based Automatic Detection for Electromechanical Equipment

Shijie Lyu

doi:10.5281/zenodo.13830975

Authors

Shijie Lyu Georgia Institute of Technology

DOI:

https://doi.org/10.5281/zenodo.13830975

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n4a02

Disciplines:

Computer Science

Subjects:

Machine Vision

References:

36

Keywords:

Automatic Detection Technology, Machine Vision, Electromechanical Equipment, Economic Benefits

Abstract

With the continuous development of industrial production, efficient and accurate inspection of mechanical equipment has become crucial for production safety, efficiency, and economic benefits. By collecting and analyzing imaging data from electromechanical equipment, effective online monitoring and fault diagnosis can be achieved, enhancing operational efficiency and accuracy while reducing manual intervention. This is a significant research direction in the field of industrial automation control. This paper begins with the fundamental principles of electromechanical equipment testing, conducting an in-depth study of its working mechanisms and providing a detailed discussion on its design and development. The main content includes the system architecture design, functional module design, and key algorithm design, laying a solid foundation for the research on automated testing of electromechanical equipment.

Author Biography

Shijie Lyu, Georgia Institute of Technology

Georgia Institute of Technology, USA.

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