No 2 (2025)

High-pressure hydrogen valves are subjected to the instantaneous impact of hydrogen flow and repeated start-stop action during service, and there is a potential risk of leakage. This paper investigates monitoring and identification of hydrogen valves leakage to ensure their operational reliability. Firstly, an acoustic signal monitoring system was built based on a high-pressure hydrogen gas-tightness test platform, and the time-domain feature of valves under different leakage conditions was analyzed. Secondly, the frequency-domain feature is extracted using a combination of Variational Modal Decomposition and Wavelet Packet Decomposition. Ultimately, the Backward Propagation Network (BP) and Convolutional Neural Network (CNN) are used to recognize patterns of acoustic signals, with the time-domain and frequency-domain parameters as feature inputs independently. The results show that the accuracy of BP and CNN networks based on frequency domain features has significantly improved, 93.33 and 91.67 %, respectively. This paper obtained the feature extraction and pattern recognition method for hydrogen valves, which provides a reference for accurate and efficient recognition of the leakage condition of high-pressure hydrogen valves in the service process.

The calculated initial and hysteresis branches of the electric voltage U (H) in a pulsed magnetic field of strength H are presented, corresponding to the branches of magnetization in the operating magnetic field and the residual magnetization of an object made of ferromagnetic material and similar branches of the used magnetic carrier (MC). The impact on an object with MN was carried out by magnetic field pulses to obtain stationary states of magnetization of an object with an internal defect, the field of which is modeled by the field of a linear inductor, the construction of spatial distributions of hysteretic interference (HI) and the creation of programs for calculating HI, which made it possible to increase the accuracy of monitoring the properties of objects.

The electrical properties of a material representing a composite of epoxy resin, magnetic fluid and carbon nanotubes are investigated. It is shown that in composites dried in the presence of a magnetic field, elongated conductive structures consisting of carbon nanotubes and magnetic fluid are formed. Their presence causes the appearance of anisotropy of the electrical properties of such composites. The anisotropy of the properties was studied by microwave waveguide methods, according to the frequency dependence of the reflection coefficient of microwave radiation from a periodic structure in which the composite under study was used as a damaged layer. It was found that the electrical properties of the composite depend on the magnitude and direction of the magnetic field induction, as well as on changes in the concentration of components in the composite. Numerical modeling was performed and the importance of taking into account the anisotropy of the electrical properties of the formed structures when calculating the integral parameters of the composite was shown.

Traditional ultrasonic testing of weld quality using the echo method often faces the problem of detecting signals from defects against the background of interference from weld irregularities. The article provides an example of how design features that usually interfere with flaw detection of welds can be used to suppress such interference and develop a highly productive and reliable ultrasonic testing technology. In this case, we are talking about ultrasonic testing welded joints of small-diameter pipes. The features of solving this problem using a chord sounding scheme are described, a brief historical background of this solution and examples of implementation in testing welded joints are given, including for inhomogeneous materials.