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

doi:10.13433/j.cnki.1003-8728.20220215

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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

机械动力学 | 更新时间：2026-07-10

- 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
- Identifying Damage of Derrick Steel Structure Based on BP Neural Network Optimized with Wavelet Packet and Genetic Algorithm
- 机械科学与技术 2024年43卷第1期页码：39-44
- 作者机构：
  
  燕山大学车辆与能源学院,河北,秦皇岛,066004
- 作者简介：
  
  韩东颖, 教授, 博士生导师, 博士,
- 基金信息：
  
  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
- DOI：10.13433/j.cnki.1003-8728.20220215
  中图分类号：
- 网络首发：2024-01-25，
  
  纸质出版：2024
- 稿件说明：
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韩东颖, 田伟, 黄岩, 等. 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.

HAN Dongying, TIAN Wei, HUANG Yan, et al. 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.
韩东颖, 田伟, 黄岩, 等. 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, et al. 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.

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。

Abstract

The damage of a derrick steel structure affects its bearing safety. In order to identify the damage location quickly and accurately

a method for identifying the damage of a derrick steel structure based on the BP neural network optimized with wavelet packet and genetic algorithm is proposed. Firstly

the excellent performance of the wavelet packet that processes non-stationary vibration signals is used to extract the original vibration signal features

and the information that characterizes the damage is obtained. Then

the data set is established through characteristic parameters to train and test the derrick steel structural damage identification method. Combined with the characteristics of the genetic algorithm

the identification method reduces the shortcomings of the traditional BP neural network. The identification method can determine the damage location without comparing the data characteristics before damage. The experimental results show that the accuracy of the derrick steel structural damage identification method is more than 90%

being higher than that of the traditional BP neural network.

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