基于加权应答神经网络的冲击声波形分析

H. Kanada, T. Ogawa, K. Mori, M. Sakata
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引用次数: 0

摘要

提出了一种利用冲击声估计复合材料弹性模量的方法。为了估计材料的疲劳程度,既要检测材料的长期疲劳成分,也要检测材料的周期性疲劳成分。我们建议使用加权回答网络来同时估计阻尼波形的周期性和长期分量。为了验证该方法的有效性,我们对复合材料冲击声引起的阻尼振动数据进行了仿真。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of impact sound waveform by answer-in-weights neural network
A method to estimate the elastic moduli of composite material from the impact sound was proposed. For estimating the degree of fatigue of the material, it is important to detect long-term components as well as periodic components. We propose to use answer-in-weights networks for estimating the periodic and long-term components of the damping waveform, simultaneously. To show the effectiveness of the method, we performed simulations on the damping vibration data occurring due to the impact sound of the composite material.
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