冲击波测试系统与动态特性补偿研究

发布时间：2018-03-30 17:08

本文选题：冲击波超压　切入点：BP神经网络　出处：《中北大学》2017年硕士论文

【摘要】：冲击波超压作为武器毁伤效能评估的重要参数,测试数据的准确性直接影响毁伤效能评估的真实性。冲击波超压存储测试仪作为战斗部爆炸冲击波信号测试仪器,其与传感器组成的系统动态性能是信号准确测试的关键。展开冲击波超压存储测试仪性能优化设计与测试系统动态特性离线补偿研究,对提升武器毁伤效能评估准确性具有现实的意义。从测试仪性能优化设计着手,阐述信号调理电路设计要点与关键器件选择方法。为提升负延时存储空间数据存储速率,提出一种双级Flash存储阵列构成的高速存储结构,并从硬件结构、存储速率分析与软件工作流程方面详细阐述了设计思路。除此之外还对负延时存储空间数据存储模式、Flash存储阵列坏块检测,以及冲击波超压关键数据无线提取的实现原理做了详实的分析论证。在研制的冲击波超压测试仪的基础上,展开测试仪与传感器构成的测试系统动态特性补偿研究。首先,介绍了测试系统动态特性描述方程、系统动态特性响应及相关指标。据此引出基于BP神经网络的系统辨识与系统动态特性补偿原理并通过实验仿真验证上述方法对测试系统动态特性补偿的有效性。然后,通过激波管产生的阶跃压力信号激励测试系统获取系统时域响应数据,由系统输入输出数据对构建的BP神经网络系统动态特性补偿模型进行离线训练。针对补偿网络训练数据不足的问题,提出用神经网络辨识的系统模型在阶跃信号簇激励下产生大量训练样本的方法,提升测试系统动态特性补偿网络的泛化能力。最后,通过对比实测冲击波数据、离线补偿数据与理论计算数据之间的误差关系验证补偿算法对测试系统动态特性的改善有效性。
[Abstract]:Shock wave overpressure is an important parameter of weapon damage effectiveness evaluation. The accuracy of test data directly affects the authenticity of damage effectiveness evaluation. The dynamic performance of the system composed with the sensor is the key to accurate signal measurement. The research on the performance optimization design of the shock wave overpressure storage tester and the off-line compensation of the dynamic characteristics of the test system is carried out. It is of practical significance to improve the accuracy of weapon damage effectiveness evaluation. Starting from the performance optimization design of the tester, the design points of signal conditioning circuit and the selection method of key devices are expounded. In order to improve the storage rate of negative delay storage spatial data, In this paper, a high speed storage structure based on two-stage Flash memory array is proposed, and the hardware structure is introduced. In addition, the memory rate analysis and software workflow are discussed in detail. In addition, the negative delay storage spatial data storage mode is detected by Flash memory array. And the principle of wireless extraction of critical data of shock wave overpressure is analyzed and demonstrated in detail. On the basis of the developed shock wave overpressure tester, the dynamic characteristic compensation of the testing system composed of the instrument and the sensor is studied. The dynamic characteristic description equation of the test system is introduced. The principle of system identification and system dynamic characteristic compensation based on BP neural network is introduced, and the effectiveness of the above method is verified by experimental simulation. The time domain response data of the system are obtained by the step pressure signal excitation test system generated by shock tube. The dynamic characteristic compensation model of BP neural network system is trained off-line from the input and output data of the system. This paper presents a method of generating a large number of training samples under the excitation of step signal cluster in the system model identified by neural network, which improves the generalization ability of the dynamic characteristic compensation network of the test system. Finally, by comparing the measured shock wave data, The error relationship between the off-line compensation data and the theoretical calculation data verifies the effectiveness of the compensation algorithm in improving the dynamic characteristics of the test system.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TJ06;TP183

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