基于频率阻抗光谱与 CARS-SVM 算法相结合的固体推进剂老化损伤定量检测方法

Leiguang Duan, Xueren Wang, Binbin Zhang, Hongfu Qiang
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引用次数: 0

摘要

固体推进剂作为固体火箭发动机的能源,实现对固体推进剂老化损伤的精确定量检测具有重要意义。本文提出了一种基于频率阻抗谱阻抗结合 CARS-SVM 算法的新方法。首先,对固体火箭发动机周围的温度、湿度和压力等环境信息进行采样,然后通过 AD5933 芯片获得推进剂相应频率的阻抗。其次,对处理后的实验数据进行异常样本检测,然后分别使用无信息变量消除(UVE)竞争性自适应加权采样(CARS)进行进一步的变量选择。最后,建立了支持向量机(SVM)、UVE-SVM 和 CARS-SVM 定量校准方法。结果表明,CARS-SVM 模型的判定系数(R2)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)分别为 0.9919、0.7540 和 0.0480。因此,结果证明固体推进剂阻抗结合 CARS-SVM 模型可有效实现固体推进剂老化损伤的高精度定量检测,为固体推进剂老化损伤在线定量检测的应用奠定了新的方法基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantitative detection of aging damage of solid propellant based on frequency impedance spectroscopy combined with CARS‐SVM algorithm
Solid propellant, as the energy source for solid rocket engines, it is of great significance to achieve accurate quantitative detection of aging damage of solid propellant. In this paper, a novel approach based on frequency impedance spectroscopy impedance combined with CARS‐SVM algorithm was proposed. First, the temperature, humidity, and pressure of environmental information around the solid rocket motor were sampled, and then the impedance at corresponding frequencies of the propellant was obtained by AD5933 chip. Second, the processed experimental data were subjected to abnormal sample detection before further variables selection using uninformative variables elimination (UVE) competitive adaptive reweighted sampling (CARS), respectively. Finally, support vector machine (SVM), UVE‐SVM and CARS‐SVM quantitative calibration methods were established. The results showed that the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) of CARS‐SVM model were 0.9919, 0.7540, and 0.0480, respectively. Therefore, the results prove that impedance of solid propellant combined with CARS‐SVM model can effectively achieve high precision quantitative detection of aging damage of solid propellant, which lays a new method for the application of solid propellants aging damage in the online quantitative detection.
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