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.
The mechanism of electrical explosive devices is to fire the resistance, semiconductor bridge, MEMS (micro-electro-mechanical system), and other hot-wire igniters by fire current excitation to make explosive agents work [
Since the electrical explosive device is the initial ignition component of the weapon, the quality requirements of the electrical explosive device are very high. At present, the commonly used reliability test methods of explosive devices are mainly success or failure experiments: lifting method [
With the development of digital technology, the electrical explosive equipment, which is based on an intelligent microprocessor, can obtain the time series of firing current and voltage, and it can be used to identify whether the fired failure explosive device can be used again. This paper mainly researches the explosive time series of semiconductor bridge electrical explosive devices. And, according to the impedance-energy curve, the explosive process is divided into positive impedance temperature rising, negative impedance temperature rising, melting, gasification, and plasma generation stages [
In summary, the explosive current and voltage time series are obtained by the intelligent electrical explosive equipment. And, aiming at the strong nonlinear and nonstationary explosive time series, this paper gets the explosive characteristic value of time series by HHT, which is mainly composed of EMD and Hilbert transformation, and identifies the explosive quality by the trained SVM. As a result, the explosive failure explosive device can be determined online whether it can be used again, without the on-site testers identifying by themselves.
The organization of this paper is as follows. In Section
When the semiconductor bridge explosive device is ignited, it quickly heats up, the dynamic impedance curve of the firing explosive device is nonlinear and stochastic, and the explosive time series is nonstationary. The traditional methods, such as the impedance method, Fourier transformation method, and wavelet transformation method, are not fitted properly with the nonstationary time series. However, HHT (Hilbert–Huang transform) is a nonstationary analysis method widely used in recent years, and HHT is mainly composed of EMD and Hilbert transform [
SVM is trained based on the Hilbert energy characteristic value of IMFs and the residual component, and the trained SVM can be used on the intelligent explosive equipment to identify the firing quality of explosive devices online.
The original time series can be decomposed into IMF components by EMD, and every IMF has to satisfy two conditions [ The number of extreme points is equal to the number of zero points, or their difference is 1 The average value of the envelopes, which is formed by the maximum and minimum, is 0
The basic EMD method is used in this paper, and for detailed steps, refer the literature [
Mutually orthogonal analytical signals can be obtained by the Hilbert transform of IMF components [
Analytical form of the IMF component is shown in the following equation:
Then, the
SVM is a commonly used classification algorithm in machine learning. The classification principle is shown in Figure
The schematic of SVM.
SVM of this paper is a two-class classification problem, and suppose that the training set of SVM is
Considering that there may be a linear inseparable situation in reality, the relaxation variable
In reality, explosive devices may be degradation or failure, when the explosive agent does not work. And, the explosive device of degradation can be used again by increasing the firing voltage, and the explosive device of failure cannot fire the explosive agent forever. So, the explosive device of degradation is still excellent performance, and the failure explosive device is still performance failure. The goal of the explosive device firing quality identification algorithm is to find whether there is a problem with the explosive device or other, and it is a two-class classification problem.
Generally, semiconductor bridge explosive devices are fired by DC power supply or capacitor energy storage, and this paper only chooses the explosive current time series as the identification signal because the explosive voltage time series keeps almost constant or decreases and does not meet EMD requirement [ Step 1: get the original explosive current time series, including the performance failure and excellent performance signal. Then, the performance of the fired explosive device can be obtained by ex post analysis of the experimenter. Moreover, the explosive current time series is normalized to get Step 2: EMD of Step 3: the Step 4: the explosive time series result yk = 1, if the performance of the semiconductor explosive device is ok. Otherwise, yk = −1. At last, the energy characteristic vector and yk are the training set of SVM, and SVM training is completed. Step 5: verifying the trained SVM by the test set.
The firing qualification identification flowchart of electrical explosive devices.
This paper takes the type of semiconductor bridge explosive devices with relatively complicated dynamic impedance as an example; these explosive devices are explosive bolts, their firing sustaining time is 100 ms, and their firing voltage is 12 V. Because the explosive devices have not been fully finalized, the failure probability of explosive devices is still high, when they detonate the explosive agent. The structure of the explosive equipment is shown in Figure
The schematic of the explosive firing equipment.
The semiconductor bridge explosive firing equipment.
In the novelty explosive bolt test, if the explosive agent fires unsuccessfully, the explosive device may be used again. So, there are excellent performance and performance failure explosive devices. And, two explosive current and voltage time series of excellent performance and performance failure explosive devices are shown in Figure
The voltage (a) and current (b) curves of normal and abnormal firing semiconductor bridge explosive devices.
From the explosive current curves, it can be seen that they are nonlinear and nonstationary signals. So, the traditional impedance method or Fourier method is not suitable for nonstationary signals, and the HHT method, which is composed of EMD and Hilbert transform, can effectively analyze the explosive current time series to reduce the nonlinear and nonstationary characteristics of explosive time series [
Explosive current time series of excellent performance and performance failure explosive devices is normalized; moreover, the EMD results of the normalized explosive current time series are shown in Figure
The EMD results of abnormal and normal semiconductor bridge explosive devices firing. (a) The result of IMF1∼IMF4. (b) The result of IMF5∼IMF7 and the residual component.
The energy characteristic values of IMF components and the residual component are obtained by Hilbert transformation as shown in Table
The energy characteristics of the IMF and residual function.
Energy | Excellent performance | Performance failure |
---|---|---|
E1 | 0.0003 | 0.0004 |
E2 | 0.0013 | 0.0007 |
E3 | 0.0007 | 0.0016 |
E4 | 0.0012 | 0.0033 |
E5 | 0.0076 | 0.0112 |
E6 | 0.0356 | 0.0136 |
E7 | 0.0078 | 0.0201 |
ER | 0.1768 | 0.3011 |
In this paper, the energy characteristics of the IMFs and residual function are inputs of classification algorithms, and the SVM method is chosen as the classification algorithm to compare with KNN (K-near neighbor) and naive Bayes [
The manufacturer provides 200 explosive devices’ test data. There are 100 explosive devices with excellent performance and others are performance failure. This paper selects 50 sets of excellent performance signals and 50 sets of performance failure signals as the training set and others are the test set. The energy characteristics of the IMFs and residual function are inputs of the classification algorithm. And, the results of 3 classification algorithms are shown in Table
The identification results of 3 classification algorithms.
Classification algorithm | Data sample | Correct times | Failure times | Accuracy rating (%) | F1-score |
---|---|---|---|---|---|
SVM | Excellent performance | 47 | 3 | 94 | 0.9307 |
Performance failure | 46 | 4 | 92 | ||
KNN | Excellent performance | 47 | 3 | 94 | 0.9216 |
Performance failure | 45 | 5 | 90 | ||
Naive Bayes | Excellent performance | 46 | 4 | 92 | 0.9109 |
Performance failure | 45 | 5 | 90 |
This paper proposes a firing quality identification algorithm to quickly identify whether there is a problem with explosive devices or other, to avoid on-site testers to personally check the failed firing explosive devices, and in order to keep them safe.
This semiconductor bridge electrical explosive device online firing quality identification algorithm decomposes the explosive current time series by EMD, obtains the energy characteristic values by Hilbert transformation of the IMF components and residual component, and in the end, the trained SVM is used to identify the firing quality of explosive devices. The identification results show the effectiveness and feasibility of the algorithm, and the firing identification algorithm can provide a basis for the evaluation of the firing quality of most electrical explosive devices, provide support for fault location of weapons and explosive systems, improvement of the storage environment of explosive devices, etc., and protect the safety of testers.
However, the SVM is linear, the identification results may not be accurate, and a deep learning algorithm is the future research direction.
The explosive voltage and current time series data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
This work was supported by the Shaanxi Provincial Department of Education Natural Science Special Scientific Research Plan Project (17JK1069).