强噪声下碳纤维复合材料疲劳损伤概率成像方法研究

孔琼英; 邓为权

doi:10.13433/j.cnki.1003-8728.20200492

强噪声下碳纤维复合材料疲劳损伤概率成像方法研究

doi: 10.13433/j.cnki.1003-8728.20200492

孔琼英^1,,
邓为权^2, ,

1.
昆明理工大学信息工程与自动化学院，昆明　650500
2.
昆明理工大学机电工程学院，昆明　650500

详细信息

作者简介:
孔琼英（1994−），硕士研究生，研究方向为结构健康监测，310345884@qq.com

通讯作者:
邓为权，讲师，博士， weiquan.deng@kust.edu.cn

中图分类号: V214.8；TP206⁺.1
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- HTML全文浏览量: 95
- PDF下载量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2020-11-26
- 刊出日期: 2022-09-05

Probability-based Diagnostic Imaging Method of Fatigue Damage for Carbon Fiber Composite Structures under Strong Noise Environment

KONG Qiongying^1
,,
DENG Weiquan^{2
, ,}

1.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
2.
Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China

摘要

摘要: 基于主动Lamb波的结构健康监测方法在实际应用中受结构振动、服役环境强噪声等干扰，使得损伤定位不准确。针对上述问题，提出了一种在强噪声背景下基于改进损伤因子的碳纤维复合材料疲劳损伤概率成像方法。本文方法利用局部加权散点平滑（Locally weighted scatterplot smoothing，LOWESS）算法对经希尔伯特变换（Hilbert transform，HT）后的含噪信号包络进行平滑处理，获得每条传感通道的飞行时间（Time of flight，ToF）；然后根据有无损伤情况下的ToF获得改进的损伤因子，并结合损伤概率成像方法实现碳纤维复合材料板内部疲劳损伤定位成像。实验结果表明，在强噪声环境下本文方法能够有效定位结构内部疲劳损伤，提高损伤定位准确性，且本文方法的损伤定位误差较现有损伤概率成像方法误差至少降低了63.7%。
- 碳纤维复合材料 /
- LOWESS /
- 强噪声 /
- 结构健康监测 /
- Lamb波
Abstract: Structural health monitoring method based on active Lamb wave will be disturbed by strong noise such as structural vibration and service environment noise, which affects the accuracy of damage location. Aiming at these problems, a probability-based diagnostic imaging method based on the improved damage factor under the environment of strong noise was proposed. The Locally Weighted Scatterplot Smoothing (LOWESS) method was used to smooth the envelope of the noisy signal after Hilbert Transform (HT), and the Time of Flight (ToF) for each channel was obtained. Then, the improved damage factor was obtained according to the ToF with or without damage, and combining with the damage probability-based diagnostic imaging method. The fatigue damage localization imaging was realized for the internal carbon fiber composite plate. The experiment result shows that this method can effectively locate the internal fatigue damage of the structure in the strong noise environment, improve the accuracy of the damage location, and the damage location error of the present method is 63.7% or more lower than that of the existing damage probability imaging method.
- carbon fiber reinforced polymer /
- LOWESS /
- strong noise environment /
- structural health monitoring /
- lamb wave

HTML全文

图 1 试件示意图和实物图

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图 2 激励信号的时域、频域图

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图 3 路径1~8的损伤散射信号及其HT变换包络

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图 4 加入SNR=3 dB的高斯白噪声的信号图

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图 5 加入SNR=0.1 dB的高斯白噪声的信号图

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图 6 加入信噪比SNR为−3 dB的高斯白噪声的信号图

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图 7 X射线图

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图 8 加入信噪比SNR为3 dB、0.1 dB、−3 dB的强噪声后现有损伤概率成像结果

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图 9 加入信噪比SNR为3 dB、0.1 dB、−3 dB的强噪声后改进损伤概率成像结果

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图 10 加入信噪比SNR为3 dB、0.1 dB、−3 dB的强噪声且阈值为0.75的现有损伤概率成像结果

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图 11 加入信噪比SNR为3 dB、0.1 dB、−3 dB的强噪声且阈值为0.75的改进损伤概率成像结果

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表 1 图1中各压电传感器的位置坐标

PZT编号	坐标/mm	PZT编号	坐标/mm
1	（128，202.5）	7	（25， 51）
2	（109，202.5）	8	（44， 51）
3	（90，202.5）	9	（63， 51）
4	（64，202.5）	10	（89， 51）
5	（45，202.5）	11	（108， 51）
6	（26，202.5）	12	（127， 51）

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表 2 不同方法峰值对应采样点

未加噪	SNR/dB	HT	误差	LOWESS	误差
995	3	1033	38	992	3
995	0.1	1050	55	995	0
995	−3	940	55	998	3

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表 3 损伤概率成像结果

SNR/dB	现有损伤概率成像方法/cm²	误差E₁	改进损伤概率成像方法/cm²	误差E₂	$\left(1-\dfrac{E_2}{E_1}\right) \times 100 \text{%}$
3	65.65	2.52	34.30	0.84	66.67%
0.1	70.95	2.81	37.59	1.02	63.7%
−3	73.32	2.94	36.12	0.94	68.03%

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参考文献(18)

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