Solid Propellant Aging Detection Method Based on Impedance Spectroscopy

Bin Bin Zhang, Guan Hua Wu, Сhao Bo Chen, Song Gao
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Abstract

Aiming at the shortcomings of large volume, high cost and long detection cycle of traditional solid propellant aging detection methods, a solid propellant aging detection method based on impedance spectroscopy is proposed. Firstly, the internal impedance of the solid propellant changes after aging, and a portable solid propellant impedance spectrum acquisition system based on impedance spectroscopy is designed based on the principle of electrochemical impedance spectroscopy, and the real and imaginary parts of the impedance spectrum are obtained. Secondly, in order to reduce the data dimension of the classification algorithm, the KPCA (Nuclear Principal Component Analysis) feature extraction algorithm is used to extract the impedance spectrum features of the solid propellant. Then, according to the impedance spectrum characteristics, the BP neural network is used for classification training, and the correspondence between the impedance spectrum and the aging time is obtained. Finally, the feasibility and effectiveness of the proposed method are verified on the physical platform, and the results show that the proposed method has the advantages of high precision and accurate classification, and can effectively predict the aging degree of solid propellant.
基于阻抗光谱学的固体推进剂老化检测方法
针对传统固体推进剂老化检测方法体积大、成本高、检测周期长等缺点,提出了一种基于阻抗谱的固体推进剂老化检测方法。首先,固体推进剂老化后内部阻抗会发生变化,根据电化学阻抗谱原理,设计了基于阻抗谱的便携式固体推进剂阻抗谱采集系统,获得了阻抗谱的实部和虚部。其次,为了降低分类算法的数据维度,采用 KPCA(核主成分分析)特征提取算法提取固体推进剂的阻抗谱特征。然后,根据阻抗谱特征,利用 BP 神经网络进行分类训练,得到阻抗谱与老化时间的对应关系。最后,在物理平台上验证了所提方法的可行性和有效性,结果表明所提方法具有精度高、分类准确等优点,能有效预测固体推进剂的老化程度。
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