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Wind power rotary support surface defect detection method

A technology of slewing support and defect detection, which is applied in image processing or feature extraction combined with machine learning for defect detection, and in the field of collecting specific texture images on the surface of wind power slewing supports, which can solve the problem of relying on manual observation, The amount of calculation is difficult to meet the real-time industrial detection, the surface of the wind power slewing support is easy to reflect light, etc., to achieve the effect of fast calculation speed, reduced calculation amount, and improved reliability and robustness

Pending Publication Date: 2020-09-25
JIANGSU UNIV OF SCI & TECH
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a method for detecting surface defects of wind power slewing supports, which solves the problems in the prior art that the surface defects of wind power slewing supports rely on manual observation, low efficiency, and high false detection rate; and the existing correct rate The high computational complexity of the algorithm is difficult to meet the real-time performance of industrial inspection, and the surface of the wind power slewing support is easy to reflect light. It is difficult for the existing methods to detect the surface defects of the wind power slewing support without interference, quickly and accurately.
The present invention utilizes the low-angle 15° irradiation direction to show the texture direction

Method used

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  • Wind power rotary support surface defect detection method
  • Wind power rotary support surface defect detection method
  • Wind power rotary support surface defect detection method

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Embodiment Construction

[0043] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the examples given are not intended to limit the scope of the present invention.

[0044] In the present invention, a specific detection algorithm is designed by using the texture features exhibited under low-angle illumination, and the image acquisition device such as figure 1 . Firstly, the image I is obtained by shooting the image I with the bar-shaped light source facing the lighting, and the angle is roughly set at 15°. The example of the collected image is as follows figure 2 , convert the image I into a grayscale image, and use Gaussian filtering to denoise the obtained grayscale image, perform morphological opening operation on the image, then use the QTSU method to binarize the image, and then use feature extraction combined with SVM train...

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Abstract

The invention provides a wind power rotary support surface defect detection method. According to the method, a low-angle 15-degree irradiation direction is utilized to show a texture direction. For ambient light interference, a low-angle strip-shaped light source can effectively relieve the ambient light interference. The detection algorithm combines a digital image processing method with SVM machine learning. The beneficial effects of the method are as follows: because an input workpiece image is simple in content and relatively single in color or gray level distribution, the algorithm can beused for avoiding the interference of adverse factors that the algorithm is very sensitive to noise and target size and cannot effectively cope with the condition that the image content is relativelycomplex, and has the characteristics of being simple in operation, high in speed and capable of realizing real-time processing. Besides, in some environments, if a vectorization form is used, operation circulation can be performed more quickly, and a multi-thread parallel processing method can also be easily adopted. The algorithm provided by the invention is small in calculation amount, high inefficiency and relatively high in accuracy.

Description

technical field [0001] The invention relates to the fields of digital image processing technology and machine vision technology, and in particular to collecting specific texture images on the surface of a wind power slewing support, and performing defect detection by image processing or feature extraction combined with machine learning for different types of textures. Background technique [0002] Wind power slewing support is one of the main products of wind power generation equipment, and its product quality must meet certain performance requirements, otherwise it will seriously affect the service life of the wind turbine. With the proposal of "Made in China 2025", green energy will be the country's primary energy choice, and wind energy, as one of the green energies, will greatly affect the use of future energy. Wind power generation has its own unique characteristics: due to long-term field work, harsh environment, and poor maintenance conditions, it is required that the...

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Application Information

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IPC IPC(8): G01N21/88G06N20/10
CPCG01N21/8851G06N20/10G01N2021/8893
Inventor 张礼华李家富蒋雨洋掌俊玮李晨洁
Owner JIANGSU UNIV OF SCI & TECH
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