Abstract: Aiming at the problem of imperfect monitoring function and low intelligence of coal transportation process in large coal storage silos, an intelligent monitoring system for coal transportation of large coal storage silos is designed, including data acquisition, robot inspection, real-time prediction, and trend prediction, fault identification, alarm management, historical playback, system management and other modules. Convolutional neural network and artificial neural network are introduced to model the equipment state, and a single indicator dimension and residual index of the equipment are proposed to judge the abnormal state of the equipment. The real-time accurate prediction and abnormal judgment of the equipment state are realized. The intelligent monitoring transformation of the Sanhe Power Plant’s coal storage silos, coal crushers, coal feeders, belts and other equipment are completed. The system improved the level of automated supervision and control, minimized operating personnel, and promoted the company’s intelligent operation. Moreover operation and maintenance costs are reduced, the production efficiency is improved.