复合GW基线启用了基于奇数值分解和ADALINE网络的大范围温度补偿方法
Yuanqiang Ren1, Zhizhong Zhang1, Shenfang Yuan1
1Research Center of Structural Health Monitoring and Prognosis, State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China.
这项研究引入了一种新的方法,使用单值分解 (SVD) 和自适应波线性神经网络 (ADALINE) 来补偿引导波 (GW) 结构健康监测 (SHM) 信号的温度变化. 该技术精确地纠正60°C范围内的GW信号,提高了SHM的可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 信号处理 信号处理
背景情况:
- 基于引导波 (GW) 的结构健康监测 (SHM) 在航空航天领域至关重要,但受到温度变化等环境因素的阻碍.
- 温度波动对GW信号产生重大影响,降低了SHM的准确性和可靠性,限制了实际应用.
- 现有的方法很难有效地弥补温度引起的信号扭曲.
研究的目的:
- 提出一种新的数据驱动方法,以补偿SHM的GW信号上的温度变化效应.
- 消除传统的ADALINE信号补偿方法中对线性相关性的依赖.
- 在不同的环境条件下提高SHM技术的准确性和可靠性.
主要方法:
- 一种混合方法,结合单数值分解 (SVD) 和自适应波器线性神经网络 (ADALINE).
- 从基线信号中提取单一向量矩阵来训练ADALINE网络,绕过线性相关性.
- 使用重量矩阵和选择的存储基线信号生成一个复合基线集,用于温度补偿.
主要成果:
- 在复合皮肤结构 (-10°C至50°C) 上进行实验验证,证明了准确的温度补偿.
- 该方法成功地在60°C的温度间隔内产生了补偿信号,使用基线为-10°C.
- 补偿信号的振幅和相位的最大误差分别低于-38 dB和-50 dB.
结论:
- 拟议的SVD-ADALINE方法有效地弥补了SHM的GW信号中的温度变化.
- 这种数据驱动的方法提高了SHM在广泛的温度范围内的可靠性和准确性.
- 该方法显示了在环境变化下需要强大的结构监测的现实应用的巨大潜力.
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