Experimental structural response of equipment mounted in store carried externally by jet type aircraft is investigated, predicted and compared with responses suggested by military standards in this study. A representative store which is similar to Mark-83 warhead with guidance units in terms of mass and geometry is used in this study. The main scope of this study is to evaluate the structural response proposed by military standards with real test conditions and also suggest a new method with an artificial neural network to predict the maximum response. Seventy-five different flight conditions are used to train the network for low and high frequency components. Also, eight flight conditions apart from the training set of flight conditions are used to test the approach. Acceleration levels are collected in real flight conditions by the data storage system. In signal processing, vibration response is expressed as power spectral density functions in the frequency domain. Procedures to predict the maximum response from measurements are determined with statistical limits in the literature. Besides the well-known limits in literature, third-order polynomial normal and logarithmic transform is used, and the performance of the different limits is compared. It is found that the military standard vibration spectrum is conservative. Distribution-free and normal tolerance limits predicted low frequency acceleration spectral density magnitudes more accurately. Their prediction performances were better than those of the other tolerance limits and that of the military standard. Third-order polynomial transform predictions are found to be reasonable with respect to normal prediction limit and envelope approach. Finally, it can be concluded that the response prediction method proposed in this article works well for Mark-83 warheads with guidance unit carried externally by jet fighter.

在本研究中，对安装在喷气式飞机外部携带的仓库中的设备的实验结构响应进行了调查、预测，并与军事标准建议的响应进行了比较。在本研究中使用了一个类似于 Mark-83 弹头的代表性存​​储，在质量和几何形状方面具有制导单元。本研究的主要范围是根据实际测试条件评估军用标准提出的结构响应，并提出一种利用人工神经网络预测最大响应的新方法。七十五种不同的飞行条件用于训练低频和高频分量的网络。此外，除飞行条件训练集之外的八个飞行条件用于测试该方法。数据存储系统在实际飞行条件下收集加速度水平。在信号处理中，振动响应表示为频域中的功率谱密度函数。从测量中预测最大响应的程序由文献中的统计限制确定。除了文献中众所周知的极限外，还使用了三阶多项式正态和对数变换，并比较了不同极限的性能。发现军用标准振动谱是保守的。无分布和正态容限更准确地预测低频加速度谱密度幅度。它们的预测性能优于其他容限和军用标准的预测性能。发现三阶多项式变换预测相对于正常预测限制和包络方法是合理的。最后，可以得出结论，本文提出的响应预测方法适用于喷气式战斗机外部携带制导单元的Mark-83弹头。