基于GA-BP神经网络的碳纤维复合芯导线压接缺陷识别方法

doi:10.19595/j.cnki.1000-6753.tces.242238

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摘要碳纤维复合芯导线因其低碳节能等特性,在输电线路的增容改造中有着良好的应用前景。但碳纤维芯棒十分脆弱,技术工艺不成熟,由于压接不良导致的断线事故时有发生,制约了该技术的推广应用。为此,该文针对断裂和少压两种严重压接缺陷,提出一种碳纤维复合芯导线压接缺陷的漏磁检测信号缺陷特征提取方法。通过实验优化,以漏磁检测信号数据中7个峰值点的幅值、21个相对位置信息和7个波形类型信息作为缺陷判断特征值,有效地提高了缺陷种类和缺陷程度识别的准确度。对碳纤维芯导线进行磁性制备,并研制相对应的漏磁检测装置,生产106根不同类型、不同程度的碳纤维芯压接缺陷样品,得到613组漏磁检测信号数据并完成特征值提取,搭建基于遗传算法（GA）的反向传播（BP）神经网络。实测数据表明,该方法可以有效地完成对碳纤维复合芯导线压接缺陷类型的识别,同时对缺陷程度的识别准确率可达到94.31%。

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	杜志叶
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关键词 ：碳纤维复合芯导线, 缺陷识别, 磁性制备, 漏磁检测, 遗传算法, BP神经网络

Abstract：Carbon fiber composite core wire has a good application prospect in the capacity improvement of the line because of its low carbon and energy saving characteristics, but the carbon fiber core rod is very fragile, the technology is immature, and the wire breakage accidents caused by poor crimping often occur, which restricts the promotion of this technology. In recent years, X-ray, acoustic imaging and infrared imaging are used in most studies, but these methods have some problems, such as unclear imaging and great influence by external environmental factors. Therefore, aiming at the two serious crimping defects of carbon fiber core fracture and carbon fiber core undercompression, this paper proposes a magnetic preparation method of carbon fiber composite core wire based on magnetic flux leakage detection technology, and designs a signal feature extraction method of MFL detection of crimping defects. Genetic algorithm optimized back propagation (GA-BP) neural network was built to recognize the defect characteristic value, which effectively improved the accuracy of magnetic flux leakage (MFL) testing detection method to identify the defect degree of carbon fiber core wires.
Firstly, the magnetic properties of carbon fiber core wires are prepared by using the film coating method. The defects of 108 groups of carbon fiber core wires were prepared by using pure iron film coated on the surface of carbon core for crimping. The fiber core undercompression defects covered by the samples ranged from 2~60 mm and the carbon fiber core fracture defects covered by the samples ranged from 0.5~5 mm. Secondly, the magnetic leakage detection system of carbon fiber core wire is established to measure 613 groups of magnetic leakage detection data. The MFL detection system can collect MFL signal and velocity data and process and display them. According to the peak value of MFL detection signal, the defects of carbon fiber core wires are divided into 16 categories according to different degrees. Thirdly, this paper optimizes the extraction method of signal characteristic values through experiments, and takes the amplitude of 7 peak points, 21 relative position information and 7 waveform type information in the magnetic flux leakage detection signal data as the characteristic values of defect judgment, effectively improving the accuracy of identification of defect type and degree. Finally, the GA-BP neural network was built and trained by the extracted eigenvalue data, and the sensitivity of key indicators such as population number and learning rate was analyzed to optimize the accuracy and operation efficiency of the model.
The experimental results show that the inductance sensor signal increases with the increase of defect degree. The fiber core undercompression defect signal of carbon fiber core increases from 3 000 at 20 mm defect to 4 000 at 40 mm defect, and the carbon fiber core fracture defect signal of carbon fiber core increases from 4 000 at 2 mm defect to 6 000 at 4 mm defect. On this basis, the amplitude, relative position and waveform type of the peak point are statistically analyzed. The relative position parameters of the normal carbon fiber core sample are higher at 1~6 positions, and the peak type parameters at 2~3 positions will show a small amount of -1. The relative position parameters of fiber core undercompression defect samples are negative at positions 7~11, and the peak type parameters at positions 2~3 have more -1 values than normal defects. The relative position parameters of carbon fiber core fracture defect samples at positions 7~11 are positive, and the peak type parameters at positions 1 are -1, and the peak type parameters at positions 3~7 are -1. By inputting 35 characteristic values of MFL detection signals into GA-BP neural network, the accuracy of defect degree recognition can reach 94.31%.
The following conclusions can be drawn from the above experimental phenomena: (1) The preparation method of carbon fiber core wire based on the film coating method can well adapt to the magnetic flux leakage detection technology and identify hidden defects. (2) The feature data extraction method of the magnetic flux leakage detection signal of carbon fiber core wire designed in this paper, through 7 groups of peak point amplitude feature data, 21 groups of peak point relative position feature data and 7 groups of peak point type feature data, The description of the key signal characteristics of MFL detection is concise and clear. (3) The GA-BP neural network trained in this paper uses 613 sets of actual magnetic flux leakage detection and measurement data to train the test model, and performs parameter optimization according to the prediction results of the model, making the prediction accuracy of the model for the defect degree of carbon fiber composite core wires reach 94.31%.

Key words： Carbon fiber composite core wire defect identification magnetic preparation magnetic leakage detection genetic algorithm BP neural network

收稿日期: 2024-12-13

PACS:

TM755

基金资助:南方电网公司科技项目（GXKJXM20220077）和国家自然科学基金项目（52377015）资助

通讯作者: 黄子韧男,2002年生,硕士研究生,研究方向为电网新型节能增容导线复合材料芯损伤检测。E-mail: 2024282070098@whu.edu.cn

作者简介: 杜志叶男,1974年生,教授,博士生导师,研究方向为智能电气设备,特高压直流输电,电磁多物理场耦合计算等。E-mail: Duzhiye@126.com

引用本文:

杜志叶, 黄子韧, 俸波, 岳国华, 廖永力. 基于GA-BP神经网络的碳纤维复合芯导线压接缺陷识别方法[J]. 电工技术学报, 2026, 41(1): 315-328. Du Zhiye, Huang Ziren, Feng Bo, Yue Guohua, Liao Yongli. Identification Method for Crimping Defects of Carbon Fiber Composite Core Wires Based on GA-BP Neural Network. Transactions of China Electrotechnical Society, 2026, 41(1): 315-328.

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