Development of Deep Learning-Driven Intelligent Circuit Testing System

Fault diagnosis in industrial circuits has always been a challenging problem. To address this issue, we have developed a deep learning-based fault diagnosis system. The system uses an improved one-dimensional convolutional neural network and Grad-CAM++ algorithm to achieve high-accuracy fault detection, classification, and localization. The system integrates high-performance hardware and optimized software, demonstrating an accuracy rate of over 97% and an average response time of 75 milliseconds in actual tests. Through model quantization and GPU acceleration, the system's performance has been significantly improved. The research results show that the system can effectively reduce production line downtime, improve industrial production efficiency, and provide a new technical solution for the field of intelligent manufacturing.