Power equipment infrared image fault positioning, identification and prediction method

Abstract

The invention discloses a power equipment infrared image fault positioning, identification and prediction method. The power equipment infrared image fault positioning, identification and prediction method comprises the following steps: 1) collecting power equipment infrared thermal image data; 2) classifying the infrared images to form a data set; 3) constructing a convolutional neural network model; 4) separating out faulty power equipment; 5) monitoring faulty power equipment in real time, and longitudinally collecting temperature data; 6) positioning a fault part, segmenting the infrared image of the power equipment, and extracting a fault area; 7) diagnosing a fault area, and judging a fault level; 8) predicting an equipment state trend; 9) uniformly outputting and displaying the information; 10) storing the fault level; 11) making four types of infrared image data sets; 12) building a target detection model and training; 13) directly detecting an infrared image of power equipmentto be detected through a target to obtain a fault position and a fault level; 14) repeating the step (5); 15) repeating the step (8); and 16) repeating the step (9) facilitating positioning of the fault position, fault level judgment and prediction of the fault equipment and giving a maintenance suggestion.

Description

technical field [0001] The invention belongs to the technical field of electric equipment fault detection, and in particular relates to a deep learning network-based infrared image fault location, identification and prediction method for electric equipment. Background technique [0002] With the rapid development of science and technology, the requirements for the power industry have also been gradually improved. At present, my country is vigorously developing smart substations and building a big data platform. Because the power equipment is in the running state for a long time, and affected by the environment and other factors, it will produce different levels of faults, which will cause certain harm to the safety and stability of the power system. Therefore, the fault detection and analysis of power equipment is very important in the smart grid. Effective monitoring of different types of power equipment, and real-time and automatic analysis of whether the power equipment ...

AI Technical Summary

Problems solved by technology

[0003]At present, most equipment inspections and fault detection still rely on on-site staff to conduct manual analysis and diagnosis. This traditional inspection fault method is not only time-consuming and dangerous , waste of manpower and material resources, and easily influenced by personal experience

Visible light is an electromagnetic wave that can be felt by the human eye, so it can play a certain role in the detection of surface defects of power equipment. However, for the analysis of internal faults and defects, because it cannot directly obtain the temperature distribution of objects, it is very important for the study of power equipment fault detection. create certain restrictions

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