Empirical mode decomposition (EMD) is an effective method to deal with nonlinear nonstationary data, but the lack of orthogonal decomposition theory and mode-mixing are the main problems that limit the application of EMD. In order to solve these two problems, we propose an improved method of EMD. The most important part of this improved method is to change the mean value by envelopes of signal in EMD to the mean value by the definite integral, which enables the mean value to be mathematically expressed strictly. Firstly, we prove that the signal is orthogonally decomposed by the improved method. Secondly, the Monte Carlo method of white noise is used to explain that the improved method can effectively alleviate mode-mixing. In addition, the improved method is adaptive and does not need any input parameters, and the intrinsic mode functions (IMFs) generated from it is robust to sifting. We have carried out experiments on a series of artificial and real data, the results show that the improved method is the orthogonal decomposition method and can effectively alleviate mode-mixing, and it has better decomposition performance and physical meaning than EMD, ensemble EMD (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In addition, the improved method is generally more time-consuming than EMD, but far less than EEMD and CEEMDAN.

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Debonding problems along the propellant/liner/insulation interface are a critical factor affecting the integrity of solid rocket motors and one of the major causes of their structural failure. Due to the complexity of interface debonding detection and its low accuracy, a method of wavelet packet transform (WPT) combined with machine learning is proposed. In this research, multi-layer structure specimens were prepared to simulate the structure of a solid rocket motor. First, ultrasonic non-destructive testing technology was used to obtain defect data. Then, WPT algorithm was employed to extract characteristic signals of the defect data. Moreover, k-nearest neighbor model, Random Forest model and support vector machine model were applied to the classification. The results showed that the accuracies of the three models were 84.67%, 90.66% and 95.33%, respectively. Positive results indicate that WPT with machine learning model exhibited excellent classification performance. Therefore, WPT combined with machine learning can achieve a precise classification of debonding defects and has the potential to assist or even automate the debonding inspection process of solid rocket motors.

Source positioning based on energy time-inverse focus is a hot subject in the sphere of shallow underground source positioning. Due to the grave wave group aliasing and the complex, irregular geological structure typical of the shallow underground explosion, the reconstruction accuracy of the energy focus is low and thus the recognition of the focus is a difficult task, ultimately leading to a low accuracy of source positioning. To address the above problems, this paper proposes a method based on deep learning energy focus recognition, whereby the process of recognizing and positioning the energy focus in an energy field is made equivalent to the end-to-end feature extraction of the energy field-energy focus. The time-variant characteristics of explosive vibration signals are put to use in the construction of an adaptive time window. First, within the time window and by combining cross-correlation and autocorrelation operations, a 3D energy field image sequence in the time-space domain is produced by grouped energy synthesis; second, a densely connected 3DCNN network is built and, through multiple layer span layer splicing, a higher repetitive use is made of the focus point features in the energy field images; third, a spatial pyramid pooling network is used to extract multi-scale features from different focus areas, which helps achieve high-precision focus recognition. Finally, numerical simulations and field tests were conducted.The results demonstrated that compared with the quantum particle swarm optimization (QPSO)-based energy focus search method, the proposed one is more effectively in recognizing the coordinates of the focus in the energy field, thus allowing high-precision localization of shallow underground sources. This method is of some engineering application value in the field of underground source positioning.

This paper proposes a new type of flexible force-sensitive structure that is resistant to gamma radiation and is made of tungsten oxide (WO3) powder, polydimethylsiloxane (PDMS), and carbon nanotube (CNT) sponge. The thickness of the sample was 2.2 mm, the middle interlayer was composed of a carbon nanotube (CNT) sponge and PDMS to form a conductive layer, and the upper and lower layers were made of tungsten oxide and PDMS, which formed a gamma-ray shielding layer. When the particle size of the tungsten oxide powder was 50 nm, 100 nm, and 1 µm, the composite force-sensitive structure exhibited better force-sensitive performance. The composite force-sensitive structure was irradiated with doses of 5, 20, 50, and 100 KGy through 60Co- rays with an energy of 1.25 MeV. The results showed that the force-sensitive characteristics changed little in significance after irradiation by different doses of gamma rays, indicating that the force-sensitive structure has good resistance to gamma radiation. This flexible stress sensor can be used in soft robots and health inspection, even in harsh environments without significant performance loss.