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Wind power blade defect identification method

A wind power blade and defect identification technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as low efficiency, large workload, and complex blade ultrasonic signals, achieve high automatic recognition rate, and avoid subjective errors. Effect

Active Publication Date: 2021-11-26
XIANGTAN UNIV
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Problems solved by technology

Ultrasonic testing has been widely used in the field of composite materials such as wind turbine blades due to its strong penetrating ability and convenient use. However, it is mainly used to detect the existence and location of bonding defects in the trailing edge of wind turbine blades. The identification of different defect modes in the web and girder mainly relies on manual experience, and comprehensive judgment is made based on the processing technology, material, structure and inspection data of the inspected workpiece; this method has a large workload and low efficiency, and the inspection level is subject to inspection The reason is that for wind power blade materials, due to the sound attenuation, scattering and reflection effects of composite materials, the blade ultrasonic signals are complex, and it is difficult to extract hidden defect information from a large number of complex ultrasonic signals. Impossible to accurately assess the condition of defects in the blade

Method used

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Embodiment Construction

[0029] The present invention will be further specifically described below in conjunction with the accompanying drawings and embodiments.

[0030] figure 1 For the prefabricated wind turbine blade samples containing defects (glass fiber cloth is glass fiber cloth) for this embodiment, the preset defect types are wrinkles, lack of glue and inclusions, and the specific defect parameters are shown in Table 1. The wind turbine blades used are made of glass fiber cloth and epoxy resin, and are formed by vacuum injection technology. The manufacturing process is to lay a layer of glass fiber and then a layer of epoxy resin. There are 37 layers in total. The first layer And the 37th layer is glass fiber cloth, and its molding thickness is 1.2mm per layer, 44.4mm in total. Three types of defects including inclusions, lack of glue and wrinkles were prefabricated in the sample, that is, the wrinkles were formed by pre-embedding cylindrical FRP between the 17th and 18th layers of glass fi...

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Abstract

The invention discloses a method for identifying defects of wind power blades, which is to perform ultrasonic detection on wind power blades, perform wavelet packet transformation on the obtained ultrasonic detection signals according to the frequency band feature window, and input the obtained energy spectrum coefficients into the BP neural network as feature vectors, The neural network outputs the corresponding defect types to realize the automatic identification of different defects of wind turbine blades. The defect recognition method provided by the invention is effective and feasible, and makes automatic recognition of wind power blade defects possible, with an average recognition rate as high as 90%.

Description

technical field [0001] The invention belongs to the technical field of intelligent detection, and in particular relates to a defect identification method of a wind power blade. Background technique [0002] As the core component of wind turbines, wind power blades have the characteristics of complex structure, various processes, and special materials, which inevitably lead to defects in the production process, such as inclusions, lack of glue, wrinkles, etc. Different types of defects have great impact on blade stiffness and strength. Therefore, in order to ensure the service life of the blade, it is very necessary to identify the type of blade defect. Ultrasonic testing has been widely used in the field of composite materials such as wind turbine blades due to its strong penetrating ability and convenient use. However, it is mainly used to detect the existence and location of bonding defects in the trailing edge of wind turbine blades. The identification of different defec...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N29/04G06N3/08
CPCG01N29/043G01N2291/023G06N3/084
Inventor 王子菡王新罗致春刘奇星
Owner XIANGTAN UNIV
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