Identifying Damage of Derrick Steel Structure Based on BP Neural Network Optimized with Wavelet Packet and Genetic Algorithm
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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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Key words:
- derrick steel structure /
- damage /
- wavelet packet /
- genetic algorithm /
- BP neural network optimization
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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 ① ② ③ ④ ⑤ 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 -
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