In order to improve the reliability of aviation electronic sub-system in UAV, the analysis method based on Failure Mode and Effects Analysis (FMEA) is put forward.The risk factors and correction measurement feasibility parameters are quantified with fuzzy theory.Then the fault modes with higher risk priority number (RPN) based on Scree-Plot method are verfied.Finally, as the ratio of RPN alteration and correction parameter F chosen for the basis of choosing correction methods, the correction method with the highest ratio is chosen to be used to maintain the UAV aviation electronic sub-system.The real example demonstrates the validity of this method.
The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In ICN, data objects are independent of location, application, storage and transport methods. Therefore, data naming is one of the fundamental issues of ICN, and research on the data naming mechanism of the LEO mega constellation for the IoT is thus the focus of this study. Adopting a fusion of hierarchical, multicomponent, and hash flat as one structure, a data naming mechanism is proposed, which can meet the needs of the IoT multiservice attributes and high-performance transmission. Additionally, prefix tokens are used to describe hierarchical names with various embedded semantic functions to support multisource content retrieval for in-network functions. To verify the performance of the proposed data naming mechanism, an NS-3-based simulation platform for LEO mega constellations for the IoT is designed and developed. The test simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high quality of service (QoS) transmission performance for the LEO mega constellation for IoT and performs better than IP-based transmission.
One of the most expensive and time-consuming activities of the software is locating the fault. Therefore, the software fault diagnosis technology and the Spectrum-based Fault Localization (SFL) is very significant to software quality assurance. Recently, the fault diagnosis technology based on the artificial intelligence theory attracts more and more attention. Multi-Agent Systems (MAS) have autonomy, intelligence and social ability, which is well-suited for diagnosing software systems, and which can easily be integrated with existing software testing schemes of software intensive equipment. In this paper, a kind of 4-layer Framework based on MAS for fault diagnosis is innovative presented. The proposed Framework is applied to real software diagnosis to validate its effectiveness.
The problem of the constant false alarm rate (CFAR) detection of bistatic space based radar (B-SBR) is addressed under the background of strong and nonhomogeneous clutter. We propose a novel and simple cell averaging (CA)-CFAR-like (CA-CFAR-L) detection method based on the Gerschgorin circle theorem for B-SBR. First, we investigate the potential of a novel test statistic TCA-CFAR-L exhibiting better properties about discrimination and CFAR using the Gerschgorin circle theorem, and the test statistic TCA-CFAR-L is constructed as the ratio of the sum of the Gerschgorin radii of the covariance matrix of the cell under test to the sum of the Gerschgorin radii of the mean covariance matrix of the reference cells. Second, we investigate the CA-CFAR-L detector for B-SBR using the constructed test statistic TCA-CFAR-L. Under the background of the K-distribution clutter model and the clutter model for B-SBR, respectively, we verify the performance of the proposed CA-CFAR-L detector, the LE mean matrix CFAR detector and the Fast Fourier Transform (FFT)–CFAR detector. Also, we analyze the experimental data for the IPIX radar. The simulation results and experimental data analysis demonstrate that the detection performance of the detector is better than the LE mean matrix CFAR detector and the FFT–CFAR detector, and the proposed method has less computational complexity. These will provide an important theoretical basis and new ideas for radar target detection in the future.
In bistatic inverse synthetic aperture radar (Bi-ISAR) system, its image resolution is lower than monostatic ISAR system. In order to solve this problem, the linear prognosis algorithm is adopted in the imaging process and the imaging algorithm based on linear prognosis is put forward. Space target Bi-ISAR imaging is taken as the example in research. The one-dimensional range profile is created through pulse compression. Before the azimuth compression, burg entropy maximum algorithm in Levinson recursive method is adopted in estimating the prognosis coefficients and the azimuth echo data. Then Fourier transformation is used to compress the azimuth data in order to get the high resolution azimuth image. This imaging method can obtain the two-dimensional image with the resolution equal to the monostatic ISAR or even higher than it. Simulation experiments verify the effectiveness and availability of the algorithm.
Traditional reliability assessment methods are generally on the basis of failure lifetime data. The failure lifetime data of high reliability and long lifetime product are obtained difficultly by life test and accelerated life test. Degradation data can provide useful information about the reliability assessment for these products. Aiming at these problems, a method of reliability prediction is presented based on failure data's reliability assessment, accelerated performance degradation and information fusion theory. Finally, this paper gives a case of accelerated performance degradation testing of 24V/2A power supply board to steady voltage to apply the method, whose results demonstrate that this method is correct and valid for reliability assessment of high reliability and long life electronic equipments.
To solve the problem that many test points are needed in radar antenna servo system when fault diagnosis is needed for this system, a new test points selection method based on Particle Swarm Optimization algorithm is researched.Test points selection is a many-target optimization problem which could easily get into local optimum value.To avoid this problem, target optimization function is constructed based on different fault modes and different fault position.The particles are constrained into a large area and the real-time particle update mode is used to make sure that the optimum value is not a local maximum or minimum value.The effectiveness of this method is validated by a simulation experiment.Equation Chapter 1 Section 1
When fault diagnosis of signal processing system of some radar is prosecuted with traditional method, many test points are involved and much time is consumed. To solve the problem, the fault diagnosis method based on particle swarm optimization (PSO) test points optimal selection algorithm is researched in this paper. PSO is used in test points optimal selection to improve the convergence velocity and the probability of converging to global optimal value. The algorithm is used in the fault diagnosis of the signal processing system of some radar to reduce the diagnosis time. Simulation experiments show that, with a relatively high diagnosis probability, this algorithm could reduce the number of test points effectively.
The advancement of test system for electronic equipment must keep pace with the complexity of modern electronic equipment. VXI bus technology has been becoming the mainstream of computer test and control, and also is the core of standardization for automatic test equipment. A kind of automatic test equipment for general electronic system based on VXI technology is presented in this paper. The hardware design and software system are discussed and the system performance is also analyzed. The software design requirement of fault diagnosis based on expert system is also introduced.
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