A Machine Vision Based Insulator Fault Detection Method

Abstract

The invention discloses an insulator fault detection method based on machine vision, which includes preprocessing the image to be processed; detecting the edge of the insulator; extracting the effective edge by using a machine learning classification method; judging the fault by detecting the shape characteristics of the insulator area and edge and performing fault detection rating. The invention can effectively detect faults of catenary insulators, the calculation amount of the algorithm is small, and the effective edge of the insulator is screened out by using the decision tree algorithm in a targeted manner, thereby avoiding various noise interferences and ensuring the accuracy of fault detection. The missed detection rate is below 1.4%, and the fault detection accuracy rate is above 98%, all of which meet the actual engineering needs. In particular, the present invention provides a feasible solution for fault detection based on machine vision with insufficient negative samples, and proposes an index for judging the fault level, which helps to make a reasonable decision based on comprehensive safety and economic factors in engineering. response.

Description

technical field [0001] The invention belongs to the technical field of image processing and analysis, and in particular relates to a catenary insulator fault detection and fault method. Background technique [0002] Catenary is an important part in the construction of electrified transmission lines. It is erected along the railway line through the pillar equipment along the line. The electric locomotive mainly obtains the electric energy required for operation through catenary transmission, so it is very important to ensure the good working condition of the catenary at all times. In the catenary system, the insulator is one of the important parts of the suspension device except the mechanical support. On the one hand, there is a sufficient distance between the live conductors of the catenary, and on the other hand, the insulation between the live conductors and the earth is ensured. Since the working environment of insulators needs to be exposed to the atmosphere for a long...

AI Technical Summary

Problems solved by technology

Damage to the ceramic body of the insulator will reduce its insulation strength. If it is not found and replaced for a long time, it will cause the porcelain bottle to break and cause other unpredictable failures in the circuit

At present, the traditional manual detection has low efficiency, high work intensity, and high risk factor. The electric field method cannot detect some external insulation defects that do not affect the electric field. These detection methods do not have certain practicability

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