Power equipment fault intelligent diagnosis method based on single-order infrared image target detection

Abstract

The invention discloses a power equipment fault intelligent diagnosis method based on single-order infrared image target detection, and the method comprises the steps: collecting a plurality of infrared images of power equipment, carrying out the preprocessing of the infrared images, and building an infrared image data set; importing the infrared image training set into a YOLOV4 convolutional neural network, and training by using a transfer learning method and a K-means++ clustering analysis method to obtain an infrared image detection network; fitting a temperature and gray scale function relationship of an infrared image temperature bar; manufacturing PC electrical equipment infrared fault diagnosis software, and embedding an infrared image detection network and the temperature gray scale function relation into the PC electrical equipment infrared fault diagnosis software, wherein target identification and analysis operation are carried out on an infrared image, so that the temperature of a corresponding point of the electrical equipment is extracted; and carrying out fault diagnosis on the equipment according to an equipment diagnosis standard in the DL / T 664-2016 electrified equipment infrared diagnosis application specification, and automatically judging whether the power equipment has a fault or not.

Description

technical field [0001] The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to an intelligent fault diagnosis method for power equipment based on single-stage infrared image target detection. Background technique [0002] During the long-term operation of substation equipment, various endogenous defects may occur. Substation operation and maintenance personnel generally use infrared temperature measurement tools to periodically inspect the equipment, and predict faults from the abnormal conditions of equipment operation as early as possible, so as to avoid The equipment suddenly stops working. This method has certain requirements for the experience and technology of operation and maintenance personnel, and has the disadvantages of incomplete coverage and low work efficiency. In the process of intelligent transformation, smart devices such as drones and inspection robots also produce a large number of inspection picture...

AI Technical Summary

Problems solved by technology

However, in the face of such a large-scale power grid, relying on manual diagnosis of equipment thermal faults makes the detection efficiency low, and the detection results are also different with the technical level, fatigue and experience accumulation of personnel, which may lead to scheduling and operation and maintenance. Make correct judgments on the fault situation of equipment, which is also extremely disproportionate to the intelligent development of the power grid

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