The electric power intelligent inspection robot is equipped with high-definition visible light camera, infrared thermal imager, sound-collecting equipment and other intelligent detection devices and intelligent analysis algorithm software to complete the control loop from rapid acquisition of all-weather data, real-time information transmission, intelligent analysis and early warning to fast decision feedback. Therefore, instead of manual inspection, the automatic detection and intelligent analysis of the state of the power equipment are realized, and the reliability of the operation of the power grid and the power equipment is improved. The use of electric power intelligent inspection robots is an important means to realize the intelligentization of power grids, and is an important direction for the future development of smart grids. Given the current research status and deficiencies at home and abroad, this paper discusses the electric power intelligent inspection robots from aspects of main technologies, cutting-edge technology, functional positioning and standard system, and discusses the research status of electric intelligent inspection robots. On this basis, future research and development direction are put forward. This paper has a guiding role and reference value for the research of electric power intelligent inspection robot.
Deep learning is good at abstract features from massive data and has good generalization ability, which has attracted more and more researchers’ attention. The Convolutional Neural Network (CNN) is a classic structure of deep learning and which is being widely and successfully used in the fields of computer vision, target detection, natural language processing, and speech recognition. Based on a detailed analysis of the current status and needs of mechanical system fault diagnosis, this paper introduces the structure of CNN and summarizes the application of CNN in the field of mechanical faults from the aspects of input data type, network structure design, and migration learning. The problems of deep feature extraction and visualization are also discussed, and finally, the difficulties in mechanical fault diagnosis are analyzed and several problems to be solved in the field of mechanical fault diagnosis based on CNN prospect.
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