With the continuous improvement of industrial automation, rapid and accurate diagnosis of equipment faults is the key to ensuring production safety and efficiency. With the advantages of non-contact sensing, real-time processing and high-precision recognition, computer vision has broad application prospects in fault diagnosis. This technology integrates image acquisition, feature extraction and deep learning models to automatically identify and classify equipment faults such as appearance damage, motion abnormalities and thermal state changes. Multi-modal image fusion further improves fault positioning accuracy under complex working conditions. In scenarios such as mine electrical equipment, construction engineering inspection cold-chain storage and unmanned aerial vehicle (UAV) inspection, its detection performance is superior to traditional methods, providing strong technical support for building an intelligent equipment operation and maintenance system and promoting the in-depth integration of industrial Internet and intelligent manufacturing.