噪声环境下防护结构受空间碎片撞击声发射信号识别方法

发布时间：2018-02-12 04:21

本文关键词： 在轨感知系统声发射碎片云干扰信号互相关希尔伯特黄变换　出处：《哈尔滨工业大学》2016年硕士论文　论文类型：学位论文

【摘要】：随着科技的进步,人们对航天事业的关注度也越来越高,探索太空的活动的增多,也给太空的空间环境带来了一定的影响,因为人类的航天活动不可避免的将产生数量非常庞大的“太空垃圾”,也被称作空间碎片,对航天器有着极其巨大的威胁性。空间站是可以在空间环境内为航天员提供工作、生活的大型航天器,需要长时间在太空中运行。在此背景下,需要开发出一种可以对空间碎片撞击进行感知的系统,即空间碎片在轨感知系统,这种系统可以对航天器受到的撞击进行实时感知、定位,并评价撞击情况。在轨感知系统运行过程中,可能采到多种突发、持续性的干扰信号,导致感知系统发生误判,因此,针对此种情况,需要对非同源的信号进行甄别,识别航天器在轨运行时受空间碎片超高速撞击的事件。本文首先对碎片云超高速撞击信号与干扰信号的波形和频谱进行对比分析。采用了互相关的概念,对标准信号与碎片云超高速撞击信号、干扰信号的频谱互相关的峰值进行对比分析。对声发射信号的处理是进行下一步的基础,利用经验模态分解的方法对信号进行处理,将得到的多种信号的固有模态函数的频谱互相关的峰值进行对比分析。由于HHT变换对处理非平稳信号有很好的效果,在经验模态分解的基础上,对信号的固有模态函数进行Hilbert变换,再进行互相关的处理,对比分析它们的频谱互相关性,提出改进算法。得到以下结论:(1)碎片云超高速撞击信号和干扰信号的频谱有重叠部分,仅从信号的波形和频谱对信号源性质的区分度不高;(2)通过对典型超高速撞击声发射信号与感知系统中获得信号的频率进行互相关的方法,计算互相关函数峰值区分超高速撞击事件;(3)对信号进行经验模态分解,将含有不同频率成分的固有模态函数的互相关函数峰值进行逐项对比,通过各分量频谱互相关的数量级识别超高速撞击事件;(4)采用HHT变换分量及重构信号的HHT变换,分别对筛选出的固有模态函数进行互相关分析,提出2种改进算法,经验证通过重构信号的HHT变换方法能够更好的识别超高速撞击事件。
[Abstract]:With the progress of science and technology, people are paying more and more attention to the space industry. The increase in space exploration activities has also brought about a certain impact on the space environment in space. Because human space activities will inevitably produce a very large amount of "space debris", also known as space debris, which is extremely threatening to spacecraft. The space station can provide work for astronauts in the space environment. Large spacecraft that live in space for a long time. In this context, we need to develop a system that can sense impact of space debris, that is, space debris in orbit. This kind of system can perceive, locate and evaluate the impact of spacecraft in real time. In the course of orbit sensing system, many kinds of sudden and persistent interference signals may be taken, which leads to misjudgment of the sensing system. In such cases, non-homologous signals need to be identified, In this paper, the waveform and spectrum of hypervelocity impact signal and interference signal of debris cloud are compared and analyzed. The concept of cross-correlation is adopted. The peak value of spectrum cross-correlation between standard signal and debris cloud hypervelocity impact signal is compared and analyzed. The processing of acoustic emission signal is the basis of the next step, and the empirical mode decomposition method is used to process the signal. The peak value of spectrum cross-correlation of the intrinsic modal function of various signals is compared and analyzed. Because HHT transform has a good effect on processing non-stationary signals, based on empirical mode decomposition, The inherent mode functions of signals are transformed by Hilbert, and then the cross-correlation is processed, and their spectral correlation is compared and analyzed. An improved algorithm is proposed. The following conclusions are obtained: 1) the spectrum of the hypervelocity impact signal and the interference signal of the debris cloud are overlapped. It is only from the signal waveform and spectrum that the signal source property is not distinguished by the method of cross-correlation between the typical hypervelocity impact acoustic emission signal and the frequency of the signal obtained in the sensing system. The peak value of cross-correlation function is calculated to distinguish hypervelocity impact events. The empirical mode decomposition of the signal is carried out, and the peak value of cross-correlation function with different frequency components is compared one by one. By using the order of magnitude of the cross-correlation of each component spectrum to identify the hypervelocity impact event, the HHT transform component and the HHT transform of the reconstructed signal are used to analyze the cross-correlation of the selected inherent modal functions, and two improved algorithms are proposed. It is proved that the hypervelocity impact events can be better identified by the HHT transform method.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：V528;V445.1

【相似文献】