Utilization of Computer Vision and Deep Learning in Fault Identification and Repair of Mechanical and Electronic Equipment

With the rapid development of computer technology, network communication, and digital signal processing, more and more professionals have begun to focus on learning and researching new knowledge and using advanced software tools to comprehensively upgrade existing equipment. In this context, deep learning machines were used to replace some of the difficulties and shortcomings in traditional teaching. This article focused on the theory, methods, and operational principles of deep neural networks. At the same time, fault diagnosis and repair strategies based on computer vision technology, machine vision, and digital signal processing were analyzed, and data simulation results were achieved through performance testing of the model. The results showed that the accuracy of the model in identifying normal equipment parts was 100%; the accuracy in identifying internal equipment parts was 94%; and the accuracy in identifying external equipment parts was 97%.