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Deep learning-based wind driven generator blade surface defect detection system and method

A technology for wind turbine and defect detection, which is applied in the field of defect detection, can solve problems such as the influence of normal operation of wind turbine blades, the impossibility of large-scale application, and the cumbersome detection process, so as to simplify learning objectives and difficulties, avoid gradient disappearance, The effect of classification accuracy improvement

Pending Publication Date: 2020-10-16
INNER MONGOLIA UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, these technologies either need to attach additional devices to the blades of the wind turbine, which may affect the normal operation of the blades of the wind turbine, or the detection process is cumbersome and the accuracy is not enough, so it cannot be used on a large scale in practice.

Method used

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  • Deep learning-based wind driven generator blade surface defect detection system and method
  • Deep learning-based wind driven generator blade surface defect detection system and method
  • Deep learning-based wind driven generator blade surface defect detection system and method

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Embodiment Construction

[0035] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0036] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and not necessarily Used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such th...

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Abstract

The invention discloses a deep learning-based wind driven generator blade surface defect detection system and a method. The system comprises an unmanned plane which is used for collecting the originalimage data of each blade of a wind driven generator according to a preset cruise track; and terminal equipment is in signal connection with the unmanned aerial vehicle and is used for inputting the original image data into a classifier constructed by a deep learning network in advance and determining the surface defect type of each blade, and the deep learning network is a ResNet network. A direct connection channel is added in the deep learning ResNet network; the input information is bypassed and transmitted to the output, so that the integrity of the information is protected, the whole network only needs to learn the difference part between the input and the output, the learning target and difficulty are simplified, a deeper network structure can be provided, the phenomenon of gradientdisappearance is avoided, and the classification precision of the surface defects of the wind driven generator blade is greatly improved.

Description

technical field [0001] The present invention generally relates to the technical field of defect detection, in particular to a system and method for detecting defects on the surface of wind turbine blades based on deep learning. Background technique [0002] A wind turbine is a device that converts wind energy into electrical energy. It is mainly composed of a wind wheel, a speed control device, a control system, a generator, a nacelle, a yaw system, and a tower. Among them, the wind rotor includes blades and hubs, and the blades are wind energy capture mechanisms for converting wind energy into their own mechanical energy. Therefore, the safe and effective operation of blades has an important impact on the power generation efficiency of wind turbines. [0003] At present, the non-destructive testing methods in the surface defect detection of wind turbine blades mainly include ultrasonic testing technology, infrared thermal imaging testing technology, vibration testing techn...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08G01N21/88
CPCG06T7/0004G06N3/084G01N21/8851G06N3/045G06F18/2414
Inventor 董朝轶杨鹏陈晓艳赵肖懿齐咏生刘利强
Owner INNER MONGOLIA UNIV OF TECH
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