It has been. As an automated validation method recommended by the IEEE Standard 1597.1/2, the FSV method has gained broad attention in the practice of computational electromagnetics modeling and simulations. This paper reviews the motivation, evolution, enhancements, and criticisms of the method over this time. The aims of this paper are to give a detailed contextualization of the development of the FSV method itself and to discuss the open questions and possible strategies of the next generation of automated validation methods.

In industrial systems, cables are used to connect various pieces of equipment. Diagnosing soft faults in cables is a consistent step to ensure the system's stable operation. The bottleneck with existing diagnostic methods is locating and evaluating multiple faults in a cable with low error. In this article, a novel fault diagnosis method is proposed to locate multiple faults and estimate their reflection coefficients; the latter is used to assess the level of the fault. First, the reflection coefficient spectrum (RCS) (the data to be processed) is modeled, the relationship with faults is elaborated, and cable model parameters are estimated. Second, a matched filter matrix is constructed to analyze the RCS to locate faults. Third, the compensation model based on the cable transfer function is constructed to compensate for the attenuation of the signal and then estimate the reflection coefficients. Finally, sidelobe analysis is performed to improve detection accuracy, and multiple reflection analysis is performed to elaborate on the complex reflection phenomenon between multiple faults and filter out their interference in detecting real faults. Experimental results show that the proposed method locates multiple faults, estimates reflection coefficients with low error, and exhibits good robustness in the presence of noise.

After the inclusion of the feature selective validation method into the IEEE Std. 1597.1/2, more and more attention has been paid to the performance evaluation and enhancements of this technique as well as broadening of applications. This paper reviews the progresses in these domains by first considering the body of work and the virtual team that is responsible for the method development, and offers conclusions about the validity, the correct implementation, and further applications of the feature selective validation technique in other domains besides the classic computational electromagnetics and electromagnetic compatibility.

Soft faults in cables may trigger short circuits and open circuits in time, in that, they ought to be detected and thus eliminated at an earliest possible stage, as to ensure safe and stable operation of the cables. A method called the time-reversal multiple signal classification (TR-MUSIC) had been proposed in the literature, which has been demonstrated to be an effective technique for locating soft faults in cables, owing to its high resolution and excellent noise robustness. However, traditional TR-MUSIC relies on a vector network analyzer for measuring the scattering matrix of cables, which adds cost and complexity to its implementation. In this regard, a new way of acquiring the desired scattering parameters is herein proposed. An arbitrary function generator is used to inject incident signals into the cable under test, and an oscilloscope is used to collect the reflected signals. After post-processing, the phase of scattering parameters can be obtained. There’s another key issue in the image of the detection results, ghost traces caused by the periodicity of Green’s function severely impact the vision saliency of the actual fault location, which limits the performance of the fault location. A step frequency variant of TR-MUSIC has been proposed, therefore, to mitigate ghost traces. Experimental results show that the fault location error of the proposed approach is smaller than 0.33% for a 51-m long coaxial cable. Moreover, in the case of introducing noise, the proposed approach can operate in situations with signal-to-noise ratios as low as –6 dB.

Stochastic collocation method (SCM), a prevailing uncertainty analysis method, has been successfully implemented in electromagnetic compatibility (EMC) simulation, especially in EMC commercial software. However, the “curse of dimensionality” problem (dimensionality means the number of uncertain variables) limits the application of the SCM. This paper proposes a novel sparse grid strategy in order to improve the computational efficiency of the SCM, especially in high-dimensionality case. In the proposed strategy, it is revealed that the number of the collocation points is in proportion to the dimensionality. By simulating two shielding effectiveness analysis examples in CST software, the feasibility of the proposed method can be presented clearly, with the help of the feature selective validation method.

As a major component of electrical wiring interconnection systems (EWISs), aircraft electrical connectors are used for the transmission of electrical signals and electrical connection between electrical equipments. It is known that electrical contact failure is the primary failure mode of connectors. Contact resistance often presents a transient characteristic that is difficult to reflect the degradation comprehensively. The electrical contact failure mechanism of connectors is studied in this article. In order to evaluate and quantify the electrical contact failure state, a high-frequency contact impedance model is established to represent the relationship between microphysical characteristics and electrical parameters. Furthermore, the influence of surface roughness and corrosion degree on the high-frequency contact impedance model is investigated by finite-element simulation and accelerated degradation experiment. The trends of contact impedance at high frequency are compared with that of contact resistance at dc condition under the circumstances of different degradation degrees. Finally, the variation law of contact impedance at high frequency is revealed, which can provide the theoretical basis for performance degradation assessment, service life prediction, and reliability evaluation for electrical connectors.