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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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

Received: 13 December 2024

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	Du Zhiye
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	Feng Bo
	Yue Guohua
	Liao Yongli

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Du Zhiye,Huang Ziren,Feng Bo等. Identification Method for Crimping Defects of Carbon Fiber Composite Core Wires Based on GA-BP Neural Network[J]. Transactions of China Electrotechnical Society, 2026, 41(1): 315-328.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.242238 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I1/315

[1] 杨长龙, 李瑞, 陈玲, 等. 碳纤维复合芯架空导线长期运行老化后性能研究[J]. 腐蚀科学与防护技术, 2019, 31(3): 365-370.
Yang Changlong, Li Rui, Chen Ling, et al.Perfor- mance variation of aluminum conductor with carbon fiber composite core after long-term service[J]. Corrosion Science and Protection Technology, 2019, 31(3): 365-370.
[2] 廖乙, 蒋兴良, 赵嘉诚, 等. 镀锌线夹大气腐蚀温升特性及表征特征量研究[J]. 电工技术学报, 2024, 39(23): 7592-7604.
Liao Yi, Jiang Xingliang, Zhao Jiacheng, et al.Study on temperature rise characteristics and feature quantity of galvanized clamps for atmospheric corrosion[J]. Transactions of China Electrotechnical Society, 2024, 39(23): 7592-7604.
[3] 张航, 杜志叶, 闵永智, 等. 基于磁膜包覆技术的碳纤维复合芯压接缺陷漏磁检测方法研究[J]. 电工电能新技术, 2024, 43(9): 82-93.
Zhang Hang, Du Zhiye, Min Yongzhi, et al.Research on magnetic leakage detection technology for crimping defects of carbon fiber composite core based on magnetic thin film coating[J]. Advanced Technology of Electrical Engineering and Energy, 2024, 43(9): 82-93.
[4] 胡雨婷, 王黎明, 尹芳辉. 碳纤维复合芯导线应用于特高压大跨越线路的脱冰工况仿真[J]. 高电压技术, 2023, 49(5): 1967-1974.
Hu Yuting, Wang Liming, Yin Fanghui.Simulation of ice-shedding of UHV long span transmission line with carbon fiber composite conductor[J]. High Voltage Engineering, 2023, 49(5): 1967-1974.
[5] 陈大兵, 魏寒来, 胡轶宁, 等. 碳纤维复合芯导线X射线图像标准化增强与缺陷检测方法[J]. 数据采集与处理, 2020, 35(4): 739-744.
Chen Dabing, Wei Hanlai, Hu Yining, et al.Standardized enhancement and detection of defects in X-ray images of carbon fiber composite core wires[J]. Journal of Data Acquisition and Processing, 2020, 35(4): 739-744.
[6] 常彬, 王海涛, 陈泓, 等. X射线在输电线路导线压接金具检测中的应用[J]. 电工材料, 2018(2): 12-15.
Chang Bin, Wang Haitao, Chen Hong, et al.Applica- tion of X-ray in detection of wire crimping fittings for transmission line[J]. Electrical Engineering Materials, 2018(2): 12-15.
[7] 万建成, 宋泽明, 黄强, 等. X射线无损检测技术在碳纤维芯导线压接中的应用研究[J]. 中国体视学与图像分析, 2020, 25(3): 242-251.
Wan Jiancheng, Song Zeming, Huang Qiang, et al.Application of X-ray nondestructive testing in crimping carbon fiber conductor[J]. Chinese Journal of Stereology and Image Analysis, 2020, 25(3): 242-251.
[8] 吕中宾, 田忠建, 刘光辉, 等. 直线扫描CT检测碳纤维复合芯导线缺陷研究[J]. 重庆大学学报, 2021, 44(5): 95-103.
Lyu Zhongbin, Tian Zhongjian, Liu Guanghui, et al.Inspecting defects of ACCC by linear scanning CT[J]. Journal of Chongqing University, 2021, 44(5): 95-103.
[9] 赵勇军, 廖圣, 杨万锐, 等. 一起基于声学及红外成像检测技术的配电网架空绝缘导线缺陷案例分析[J]. 电气技术, 2022, 23(12): 95-99.
Zhao Yongjun, Liao Sheng, Yang Wanrui, et al.Analysis of a case of defect of overhead insulated conductors in distribution network based on acoustic and infrared imaging detection technology[J]. Electrical Engineering, 2022, 23(12): 95-99.
[10] 孙宇微, 吕安强, 谢志远. 电缆及其附件局部放电超声波检测技术研究进展[J]. 电工电能新技术, 2022, 41(9): 47-57.
Sun Yuwei, Lü Anqiang, Xie Zhiyuan.Research progress of partial discharge ultrasonic detection technology for cable and its accessories[J]. Advanced Technology of Electrical Engineering and Energy, 2022, 41(9): 47-57.
[11] 李君华, 梁世容, 张兴森. 耐张线夹压接质量的相控阵超声检测[J]. 无损检测, 2020, 42(12): 35-38, 51.
Li Junhua, Liang Shirong, Zhang Xingsen.Phased array ultrasonic testing of the quality of tension clamp[J]. Nondestructive Testing Technologying, 2020, 42(12): 35-38, 51.
[12] 张文峰, 彭向阳, 陈锐民, 等. 基于无人机红外视频的输电线路发热缺陷智能诊断技术[J]. 电网技术, 2014, 38(5): 1334-1338.
Zhang Wenfeng, Peng Xiangyang, Chen Ruimin, et al.Intelligent diagnostic techniques of abnormal heat defect in transmission lines based on unmanned helicopter infrared video[J]. Power System Technology, 2014, 38(5): 1334-1338.
[13] 彭向阳, 梁福逊, 钱金菊, 等. 基于机载红外影像纹理特征的输电线路绝缘子自动定位[J]. 高电压技术, 2019, 45(3): 922-928.
Peng Xiangyang, Liang Fuxun, Qian Jinju, et al.Automatic recognition of insulator from UAV infrared image based on periodic textural feature[J]. High Voltage Engineering, 2019, 45(3): 922-928.
[14] 王永伟, 朱波, 曹伟伟, 等. 碳纤维复合材料导线X射线无损检测技术开发及应用[J]. 化学分析计量, 2014, 23(5): 72-74.
Wang Yongwei, Zhu Bo, Cao Weiwei, et al.Development and application of non-destructive testing technology on ACCC[J]. Chemical Analysis and Meterage, 2014, 23(5): 72-74.
[15] 徐志伟, 陆桂来, 朱波, 等. X射线探测在输电线路碳纤维导线中的应用[J]. 黑龙江电力, 2020, 42(3): 215-217.
Xu Zhiwei, Lu Guilai, Zhu Bo, et al.Application of X-ray detection in carbon fiber conductor of transmission line[J]. Heilongjiang Electric Power, 2020, 42(3): 215-217.
[16] 郑含博, 胡思佳, 梁炎燊, 等. 基于YOLO-2MCS的输电线路走廊隐患目标检测方法[J]. 电工技术学报, 2024, 39(13): 4164-4175.
Zheng Hanbo, Hu Sijia, Liang Yanshen, et al.A hidden danger object detection method for transmis- sion line corridor based on YOLO-2MCS[J]. Trans- actions of China Electrotechnical Society, 2024, 39(13): 4164-4175.
[17] Huang Songling, Peng Lisha, Sun Hongyu, et al.Deep learning for magnetic flux leakage detection and evaluation of oil & gas pipelines: a review[J]. Energies, 2023, 16(3): 1372.
[18] 杜志叶, 阮江军, 余世峰, 等. 油管漏磁检测的有限元建模技术研究[J]. 中国电机工程学报, 2007, 27(27): 108-113.
Du Zhiye, Ruan Jiangjun, Yu Shifeng, et al.Research on finite element method modeling techniques of magnetic flux leakage testing for oil pipe[J]. Procee- dings of the CSEE, 2007, 27(27): 108-113.
[19] 杨国林, 蒋兴良, 廖乙, 等. 输电线路单导线自由扭转覆冰动态仿真研究[J]. 电工技术学报, 2024, 39(13): 4079-4089.
Yang Guolin, Jiang Xingliang, Liao Yi, et al.Simulation study of the free torsional icing on single conductors of transmission lines[J]. Transactions of China Electrotechnical Society, 2024, 39(13): 4079-4089.
[20] Wang Hongyao, Tian Jie, Li Xiaowei, et al.Inspection of mine wire rope using magnetic aggrega- tion bridge based on magnetic resistance sensor array[J]. IEEE Transactions on Instrumentation and Measure- ment, 2020, 69(10): 7437-7448.
[21] Liu Xiucheng, Xiao Junwu, Wu Bin, et al.A novel sensor to measure the biased pulse magnetic response in steel stay cable for the detection of surface and internal flaws[J]. Sensors and Actuators A: Physical, 2018, 269: 218-226.
[22] 陈立珂, 刘瑞芳, 李知浩, 等. 电缆参数对变频驱动电机轴电压和轴电流的影响[J]. 电工技术学报, 2024, 39(15): 4755-4766, 4793.
Chen Like, Liu Ruifang, Li Zhihao, et al.The influence of cable parameters on the bearing voltage and bearing current of the variable frequency drive motor[J]. Transactions of China Electrotechnical Society, 2024, 39(15): 4755-4766, 4793.
[23] 杨志军, 张博文, 蒙昌彭, 等. 钢丝绳芯传送带损伤的漏磁检测[J]. 无损检测, 2024, 46(8): 18-23.
Yang Zhijun, Zhang Bowen, Meng Changpeng, et al.Magnetic leakage detection for damage of wire rope core conveyor belts[J]. Nondestructive Testing Technologying, 2024, 46(8): 18-23.
[24] 黄友聪, 郑钟楠, 林梓圻, 等. 基于频域反射法的高压电缆阻水缓冲层缺陷定位研究[J]. 绝缘材料, 2024, 57(6): 86-95.
Huang Youcong, Zheng Zhongnan, Lin Ziqi, et al.Research on locating defects in water-blocking buffer layer of high-voltage cables based on frequency domain reflection method[J]. Insulating Materials, 2024, 57(6): 86-95.
[25] 左万君, 戴西斌, 吴昌玉. 漏磁检测在管道损伤探测中的应用[J]. 无损检测, 2024, 46(3): 56-63.
Zuo Wanjun, Dai Xibin, Wu Changyu.Application of magnetic flux leakage testing in pipeline damage detection[J]. Nondestructive Testing Technologying, 2024, 46(3): 56-63.
[26] Li Shengping, Bai Libing, Zhang Xu, et al.A defect opening profile reconstruction method based on multidirectional magnetic flux leakage detection[J]. Measurement, 2024, 233: 114701.
[27] Ren Liyuan, Liu Zhiliang, Zhou Jianguo.Shaking noise elimination for detecting local flaw in steel wire ropes based on magnetic flux leakage detection[J]. IEEE Transactions on Instrumentation and Measure- ment, 2021, 70: 1-9.
[28] Zheng Dong, Qian Zhendong, Liu Yang, et al.Prediction and sensitivity analysis of long-term skid resistance of epoxy asphalt mixture based on GA-BP neural network[J]. Construction and Building Materials, 2018, 158: 614-623.
[29] Xu F, Jiang Z, Jiang Q, et al.Damage detection and assessment of broken wires in cables of a bridge based on magnetic flux leakage[J]. Experimental Techniques, 2023, 47(4): 907-920.
[30] Ngwenyama M K, Gitau M N.Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers[J]. Scientific Reports, 2024, 14: 6080.
[31] 孙廷玺, 方义治, 郑晓东, 等. 电力电缆局放在线监测神经网络自动识别精度的提升方法[J]. 高压电器, 2024, 60(7): 210-220.
Sun Tingxi, Fang Yizhi, Zheng Xiaodong, et al.Improvement method of automatic identification accuracy of on-line monitoring neural network for partial discharge of power cable[J]. High Voltage Apparatus, 2024, 60(7): 210-220.
[32] Ding Guoping, Hou Shijing.CFRP drive shaft damage identification and localization based on FBG sensing network and GWO-BP neural networks[J]. Optical Fiber Technology, 2024, 82: 103631.