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小波包与遗传算法优化BP神经网络相结合的井架钢结构损伤识别

韩东颖 田伟 黄岩 朱国庆

韩东颖, 田伟, 黄岩, 朱国庆. 小波包与遗传算法优化BP神经网络相结合的井架钢结构损伤识别[J]. 机械科学与技术, 2024, 43(1): 39-44. doi: 10.13433/j.cnki.1003-8728.20220215
引用本文: 韩东颖, 田伟, 黄岩, 朱国庆. 小波包与遗传算法优化BP神经网络相结合的井架钢结构损伤识别[J]. 机械科学与技术, 2024, 43(1): 39-44. doi: 10.13433/j.cnki.1003-8728.20220215
HAN Dongying, TIAN Wei, HUANG Yan, ZHU Guoqing. Identifying Damage of Derrick Steel Structure Based on BP Neural Network Optimized with Wavelet Packet and Genetic Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(1): 39-44. doi: 10.13433/j.cnki.1003-8728.20220215
Citation: HAN Dongying, TIAN Wei, HUANG Yan, ZHU Guoqing. Identifying Damage of Derrick Steel Structure Based on BP Neural Network Optimized with Wavelet Packet and Genetic Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(1): 39-44. doi: 10.13433/j.cnki.1003-8728.20220215

小波包与遗传算法优化BP神经网络相结合的井架钢结构损伤识别

doi: 10.13433/j.cnki.1003-8728.20220215
基金项目: 

国家自然科学基金项目 51875500

河北省人社厅留学人员科技活动项目 C20190516

详细信息
    作者简介:

    韩东颖, 教授, 博士生导师, 博士, dongying.han@163.com

  • 中图分类号: TG156

Identifying Damage of Derrick Steel Structure Based on BP Neural Network Optimized with Wavelet Packet and Genetic Algorithm

  • 摘要: 井架钢结构损伤影响其承载安全性,为快速、准确对损伤位置进行识别,提出小波包与遗传算法优化BP神经网络相结合的井架钢结构损伤识别方法。首先利用小波包处理非平稳振动信号的优良性能对原始振动信号进行特征提取,获得表征井架钢结构损伤的信息;再通过特征参数建立数据集训练并测试井架钢结构损伤识别模型,该模型结合遗传算法自身特点改善了传统BP神经网络的不足。本文识别方法不需要损伤前的数据特征进行对比,便可对损伤位置进行确定。经过对石油井架钢结构模型实验验证:该方法对井架钢结构损伤识别准确率超过90%,相对于BP网络识别准确率以及识别速度均有所提高。
  • 图  1  小波包分解原理图

    Figure  1.  Schematic diagram of wavelet packet decomposition

    图  2  GA-BP神经网络算法流程

    Figure  2.  GA-BP neural network algorithm flow

    图  3  传感器布置及损伤设定位置

    Figure  3.  Sensor layout and damage setting position

    图  4  位置①处销钉损伤3号传感器时域信号图

    Figure  4.  Time domain signal diagram of pin damage No. 3 sensor at position ①

    图  5  位置①处销钉损伤3号传感器频域信号图

    Figure  5.  Frequency domain signal diagram of sensor No. 3 at position ①

    图  6  小波包三层分解前3频段信号图

    Figure  6.  Signal diagram of the first 3 frequency bands before the three-layer decomposition of the wavelet packet

    图  7  误差与遗传代数变化图

    Figure  7.  Error and genetic algebraic variation diagram

    图  8  GA-BP网络识别结果

    Figure  8.  GA-BP network identification results

    图  9  BP网络与GA-BP网络识别结果对比

    Figure  9.  Comparison of recognition results between BP network and GA-BP network

    表  1  待测单损伤分布位置

    Table  1.   Distribution location of single damage to be measured

    损伤位置 销钉1 销钉2 销钉3 斜撑1 斜撑2
    位置编号
    下载: 导出CSV

    表  2  网络与GA-BP网络的识别结果统计

    Table  2.   Statistics of recognition results of GA-BP network and GA-BP network

    损伤类型 销钉1 销钉2 销钉3 斜撑1 斜撑2
    损伤标签 1 2 3 4 5
    测试样本数 12 13 13 12 10
    BP正确识别 9 11 12 9 8
    GA-BP正确识别 12 12 13 11 8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-10
  • 刊出日期:  2024-01-25

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