This study describes a technique for evaluating partial discharges at fast impulse voltages. Fast transient voltages are increasingly present in power equipment and industrial environments due to the ubiquitous application of power electronics switching devices. Special attention should be paid to the dangers caused by the accumulative effect of repetitive impulse voltages on electrical insulation. This study describes a method of analysing partial discharges occurring during impulse voltage stimuli. A repetitive train of pulses is applied to a dielectric specimen and the accumulated PD pattern is obtained. This study focuses on the reverse discharges which occur during the tail part of the impulse voltage waveform acquired in time-resolved mode. Investigations of the partial discharges in the wave tail can potentially lead to greater degradation of the insulation due to the formation of longer, repetitive sequence occurring along the decaying impulse voltage tail. Experiments were performed which investigated the influence of rise time, fall time and peak voltage on discharge pattern reshaping, intensity and time to inception on the tail. In the future, such an approach might be further developed for diagnostic applications and for extended factory tests.

This study presents an analysis and the design of a new flat-type position sensor with an external armature. One excitation coil and two antiserially connected pickup coils are used in the stationary part. Solid iron segments or steel lamination segments are used for the moving armature. The proposed position sensor was modelled using linear movement. A two-dimensional finite-difference method was developed and was used for fast analysis for optimising the sensor. The induced eddy currents in the solid armature were taken into account in the finite-difference analysis. The finite-difference calculations were compared with 2D and 3D finite-element method simulations and with experimental results. The sensor has a total error of 0.23 mm root-mean-square for 36 mm range without any compensation. Unlike previous designs, the authors’ new sensor has no moving coil.

To effectively improve the computation efficiency in the strapdown inertial navigation system (SINS) alignment process, the authors propose a marginalised adaptive sparse-grid quadrature filter (MASGQF). The filtering method combines the virtues of marginalisation technique and accuracy level switching structure to decrease the computational burden and improve the filtering precision simultaneously. With the adaptation criterion and the predefined tolerance value, different integration rules corresponding to the different accuracy levels nested in the proposed filtering method can be adjusted autonomously. It is proved that the MASGQF is exponentially bounded by theoretical analysis. The performance comparison of different filtering methods is demonstrated by the numerical results of the simulation and offline experiment. On the basis of the data performance of the online test, the validity of the proposed MASGQF is verified in the SINS alignment application.

During the recursive calculate process of the cubature Kalman filter (CKF), the covariance matrix tends to lose positive definiteness and noise statistical characteristics are inaccurate, which results in inaccurate filtering or even filter divergence. This study presents an improved algorithm based on the CKF. The algorithm combines the square root filter algorithm and the Sage–Husa maximum a posterior noise estimator, which can ensure the non-negative determination and symmetry of the covariance matrix and has the ability to deal with unknown and time-varying noise statistical characteristics in the filtering process adaptively. In the multi-dimension system, the noise covariance matrix may dissatisfy non-negative definiteness and result in filter divergence, and then the noise covariance matrix estimator is improved. The analysis is verified by state estimation example of the non-linear system, compared with the standard CKF, the accuracy of the adaptive square root CKF (ASRCKF) state estimation is increased by 63.13, 63.88, and 42.71%, respectively. Finally, the effectiveness of the ASRCKF is verified by the state estimation of the permanent magnet linear synchronous motor.

The inverse problem of electromagnetic (EM) non-destructive testing (NdT) consists of reconstructing material defect parameters invoking EM field measurements. Uncertainties of the configuration (e.g. imprecise constitutive and geometrical parameters) are inevitably present; hence, the reconstructed defect parameters are also uncertain. In this study, the different sources of uncertainty are ranked by means of sensitivity analysis. The model-based inversion (involving EM simulation) is computationally demanding; moreover, sensitivity analysis usually requires a vast number of repeated runs of the inversion. To overcome the computational complexity, surrogate models are applied at different levels. Interpolation on a sparse grid is used as a surrogate model of the EM simulation. The sensitivity of the reconstructed defect parameters concerning configuration uncertainties is characterised by means of Sobol indices. The Sobol indices are obtained from a polynomial chaos expansion surrogate model of the entire inversion scheme. A numerical example drawn from eddy-current NdT is thoroughly analysed to illustrate the proposed methodology and to demonstrate its performance.