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With the increase of space activities, the safety detection of equipment in supercold environment becomes more and more important. However, the safety detection of equipment in liquid hydrogen environment is an unsolved worldwide problem. In this article, we present a new method to detect acoustic emission (AE) signals in liquid hydrogen environment, which can be used to confirm the safety and reliability of the equipment working in liquid hydrogen. First, the principle of mandrel-based fiber-optic ring (MBFOR) AE sensor and the corresponding demodulation method are introduced. Second, the effects of physical parameters, such as the length of the sensing fiber, the diameter and material of the mandrel, and the number of fiber winding layers on the sensor's sensitivity, are studied by experiment. Finally, the AE source localization accuracy test of the FOR AE detection system at low temperatures and the adaptability test of the proposed method in −252.8 °C liquid hydrogen are carried out. The results show that the proposed MBFOR sensor and the multichannel AE detection system can complete the defects detection of aerospace pressure vessel in liquid hydrogen. Certainly, it can be used for safety detection for other spacecraft equipment in harsh environments.

A heightened demand for improved printed circuit board (PCB) fault detection arises with the increasing integration and enhanced functionality of PCBs. Traditional visible light image analysis demonstrates accuracy and safety advantages in nonintrusive detection. However, it exhibits limitations in detecting obscured faults or lack of visible defects. The study proposes a fault detection framework based on infrared thermal imaging, aiming to enhance the practicality and engineering efficiency of PCB fault detection. This article introduces an infrared-thermography-based framework for anomaly detection and fault classification of PCBs. The framework encompasses preprocessing infrared thermal images, extraction of multimodal feature vectors (MMFVs), density-based anomaly detection (DAD), and fault classification based on deviation matrix clustering. The framework extracts multimodal features from the residual temperature scalar fields and residual temperature gradient vector fields, using multiscale detection of global and local images for fault classification. Experimental validation on a two-phase drive (TPD) circuit illustrates a significant enhancement in PCB anomaly detection and fault classification accuracy compared with existing feature extraction techniques. This research provides an innovative and practical PCB manufacturing and maintenance tool, effectively elevating anomaly detection accuracy and operational feasibility in engineering practices.

Wide band-gap field-effect power devices, such as gallium nitride (GaN) high electron mobility transistors are being widely used for a better system-level performance as a result of superior figure-of-merits in power-conversion application. But they could also suffer from more severe bridge-leg crosstalk issue in fast-switching operation due to their special device features. To suppress the bridge-leg crosstalk, an explicit model and theoretical analysis which could guide collaborative optimization of multiparameter is required. However, the superposition of two noise excitations (di/dt and dv/dt) and high order circuit structure bring difficulties to modeling and analysis. By the physical behaviors of different switching stages, the generation of crosstalk voltage is analyzed. This article proposes an analytical model of crosstalk voltage, with the explicit representation of parasitic parameters. Based on the model, an exhaustive investigation into the correlation of parameters on the crosstalk voltage is conducted. Further, a scheme for multiparameters collaborative optimization is presented, the safe operational area for crosstalk suppression has been determined. The superiority of the proposed model and the effectiveness of the optimization scheme are verified with simulations and experiments on a double pulse test platform. The model and analysis method can guide the selection of GaN devices and the design of parameters, which is convenient for the pre-test of printed circuit board before power on.

Using magnetic field data to characterize the state of radio frequency (RF) printed circuit board (PCB) has been widely used in circuit state detection and fault identification. However, it may be difficult to measure the complete state of magnetic field radiation, so the characteristics of PCB may be challenging to establish, which makes the calculation method of reconstructing other radiation information of PCB with existing magnetic field information urgently needed. Based on the basic radiation principle of the electromagnetic field, we propose an algorithm that has advantages in reconstructing the magnetic field radiation state, in which the new single-layer iterative (NSLI) algorithm provides a more reasonable optimization range and field-source conversion matrix for the hybrid particle swarm optimization (HYPSO) algorithm, and converts the inverse problem of the large matrix into a forward optimization problem, further ensuring the accuracy and objectivity of the algorithm. According to the known magnetic field amplitude, this article uses a two-phase drive (TPD) circuit at 50-MHz frequency as a device under testing (DUT) to verify the proposed algorithm’s feasibility by reconstructing the complete 3-D magnetic field component radiated from the DUT and estimating the unknown magnetic field amplitude on the predicted plane. The specific experimental data show that when the magnetic field amplitude is known, the relative errors of the reconstructed magnetic field on the $x$ -, $y$ -, and $z$ -components are 1.9%, 0.02%, and 0.06%, respectively. In the meantime, under the actual measurement environment with a maximum relative uncertainty of 0.59%, the relative error of the magnetic field amplitude of the unknown predicted plane is 6.15%.