Wind turbine blade crack diagnosis method and device

Abstract

The invention provides a wind turbine blade crack diagnosis method and device, and relates to the technical field of wind turbine fault diagnosis, and the method comprises the steps: obtaining a plurality of blade images, continuously collected by binocular camera equipment at a preset frame rate, of wind turbine blades under the application of preset excitation; arranging a plurality of marks onthe wind turbine blades; identifying a mark on the leaf image and determining a space coordinate of the mark so as to generate space vibration data of the mark; intercepting continuous preset frame data from the marked spatial vibration data to serve as initial data; wherein each frame of leaf image corresponds to one frame of data; correspondingly mapping the vibration data of each mark in the initial data in the X-axis direction, the Y-axis direction and the Z-axis direction to R, G and B components on the image so as to generate a fused image fused with the vibration information of each mark; and diagnosing the crack of the wind turbine blades according to the fused image. The wind turbine blade crack diagnosis method has a good effect in wind turbine blade crack diagnosis.

Description

technical field [0001] The present application relates to the technical field of fault diagnosis of wind turbines, in particular to a method and device for diagnosing cracks in wind turbine blades. Background technique [0002] Wind turbine blades are the key components for wind turbines to capture wind energy. During operation, the safety and reliability of the blades have a very important impact on the entire wind turbine, and its performance will directly affect the stable operation of the entire wind turbine. Due to the long-term impact of multiple loads on the blade and the corrosion of nature, it usually leads to the failure of the blade to cause fatigue cracks or fractures. At the same time, repairing cracks in wind turbine blades requires a lot of downtime. Therefore, regular diagnosis of the health status of wind turbine blades has significant economic benefits. [0003] At present, most of the samples constructed in fault diagnosis research are based on the vibr...

AI Technical Summary

Problems solved by technology

In this patent document, cracks are identified through blade images, and in practical applications, the surface texture characteristics of early cracks are not obvious and other factors are limited.

Although the deep learning method has been applied in the field of fault diagnosis, there are few effective features obtained directly through the leaf image extraction method, which affects the accuracy and accuracy of diagnosis.

[0005] In summary, there are certain defects in the existing methods for diagnosing cracks in wind turbine blades.

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