卷簧式柔性取样器的取样头-岩石接触原位辨识(英文)  

In-Situ Sampling Head-Rock Contact Identification for a Coiling-Type Sampler

作　　者：凌云[1] 宋爱国[1] 陆伟[2] 

机构地区：[1]东南大学仪器科学与工程学院,南京210096 [2]南京农业大学工学院,南京210031

出　　处：《宇航学报》2012年第10期1536-1543,共8页Journal of Astronautics

摘　　要：取样头和岩石接触问题与月壤取样的成功与否直接相关,基于小型卷簧式月壤取样器的柔性取样臂结构,提出了振动取样法:在取样过程中采集振动信号进行数字信号处理与分析,达到对取样头-岩石接触的辨识。首先对振动信号进行采集、消除趋势项,然后利用小波多分辨分析去噪,采用现代功率谱中基于AR模型的Burg法对振动信号进行特征提取,最后采用二分类支持向量机对取样头是否接触到岩石进行识别。实验结果证明,卷簧式取样器的振动信号分析能够对取样头-岩石接触问题进行较好的辨识。It will lead to failure of planet soil sampling if the sampling head is in contact with a big-size rock, So identifying this situation is significant in the process of planet sampling. In this paper, a vibration sampling method is proposed to solve this problem. The vibration signals are acquired and analyzed in the process of sampling experiments, in order to achieve the sampling head-rock contact （SHRC） identification by taking advantage of the special structure of the mini coiling-type sampler, which takes a flexible coiling spring as its sampling arm. First of all, the vibration waves are generated by a vibration motor and the vibration signals are acquired by an acceleration sensor. Then, the multi-resolution wavelet is used to filter the vibration signals. The power spectrum estimation method Burg is introduced to extract the SHRC- related features of vibration signals. At last, the status of whether the sampling head touching the rock is identified by the C- SVM （Clustering Support Vector Machine）. Experimental results show that the SHRC identification problem can be solved successfully by the analysis of the vibration signals.

关 键 词：卷簧式月壤取样器 振动控制 小波多分辨分析 功率谱 支持向量机 

分 类 号：V19[航空宇航科学与技术—人机与环境工程]