When qualified explosive devices fire the explosive agent unsuccessfully, on-site testers cannot diagnose fast and accurately whether it is the firing quality problem of the electrical explosive devices or explosive agent by using traditional test methods. And, if the explosive agent is fired unsuccessfully, generally, the only way is to test the explosive device by on-site testers themselves. In order to protect the on-site testers’ safety, this paper proposes an electrical explosive device firing quality identification algorithm based on HHT (Hilbert–Huang transform) of the explosive time series. Obtaining an explosive current time series during the firing process of electrical explosive devices by the explosive equipment, the IMFs (intrinsic mode functions) and a residual function of the explosive current time series are obtained by EMD (empirical mode decomposition), the feature vector, which is the energy characteristic values of the IMFs and residual function by Hilbert transformation, is the input of SVM (support vector machine), and the fired failure explosive device is identified as an excellent performance product or performance failure product by the trained SVM. Finally, semiconductor explosive devices are tested to verify the proposed algorithm, and the results show that the EMD-SVM algorithm can identify effectively the firing quality of firing explosive devices.

中文翻译：

---

半导体桥电炸药在线射击质量识别算法

当合格的爆炸装置无法成功发射爆炸剂时，现场测试人员无法使用传统的测试方法快速，准确地诊断是电气爆炸装置还是爆炸剂的发射质量问题。而且，如果炸药没有成功发射，通常，唯一的方法是由现场测试人员自己测试爆炸装置。为了保护现场测试人员的安全，本文提出了一种基于爆炸时间序列的HHT（希尔伯特-黄变换）的电爆炸装置点火质量识别算法。在爆炸装置射击电子爆炸装置的过程中获得爆炸电流时间序列，通过EMD（经验模态分解）获得爆炸性电流时间序列的IMF（本征模函数）和残差函数，并通过希尔伯特变换输入特征矢量（即IMF和残差函数的能量特征值） SVM（支持向量机）的性能，并且射击失败爆炸装置被训练有素的SVM识别为出色性能产品或性能失败产品。最后，对半导体爆炸装置进行了测试，以验证该算法的有效性，结果表明，EMD-SVM算法可以有效地识别爆炸装置的点火质量。经过训练的SVM将点火失败的爆炸装置确定为性能优异的产品或性能失效的产品。最后，对半导体爆炸装置进行了测试，以验证该算法的有效性，结果表明，EMD-SVM算法可以有效地识别爆炸装置的点火质量。经过训练的SVM将点火失败的爆炸装置确定为性能优异的产品或性能失败的产品。最后，对半导体爆炸装置进行了测试，以验证该算法的有效性，结果表明，EMD-SVM算法可以有效地识别爆炸装置的点火质量。