Search

The position and shape information of defects can be determined by using ultrasonic guided wave to invert the defects in the plate structures. Aiming at the problem that the travel time imaging method is not effective in the low frequency range, an ultrasonic guided wave defect inversion imaging method based on wave field is proposed. According to the wave equation, the imaging principle of beamforming is derived, and the scattering field data in Born approximation are used to coherently stack the values of pixels in the imaging area to obtain the position and shape of defects. Through the mapping relationship between beamforming and Fourier diffraction theorem in frequency domain, the results of beamforming are transformed into diffraction tomography images, and some clearer inversion images are obtained. Aiming at the problems of artifact and noise in diffraction tomography, the two‑dimensional variational mode decomposition (2D‑VMD) method is used to denoise the images, which can effectively remove the artifact and the burr of defect contour edge, and further improve the imaging resolution. The inversion results show that the proposed method can accurately reconstruct the location, size and shape of the thinning defects on the aluminum plate with high resolution. [ABSTRACT FROM AUTHOR]

In surface metrology, filtration is one of the key operations which is used to decompose the components of the surface and extract a scale-limited surface for further assessment. Although many types of filters have been proposed and some of them have been standardized in ISO16610 serials, the classic surface topography filters often inherit the mode mixing and boundary distortion problems that may lead to surface texture verification failures. This study proposes a new filter algorithm for the surface texture analysis, and it is based on the extended bi-dimensional variational mode decomposition (BVMD), named EBVMD. BVMD is widely used in the field of image processing and it is probably first-time to be used in the field of surface texture analysis. It consists of three steps. Firstly, a coarse-grained parallel genetic algorithm is applied in the devised model to select of optimal penalty factor and decomposition number of the bi-dimensional variational mode, and the best set of modes is gained. Secondly, the instantaneous wavelength value of each mode is obtained by calculating the corresponding isotropic monogenic signals. Finally, the scale-limited surface is extracted by the mean instantaneous wavelength. Experiments are conducted to verify the feasibility of the proposed filter on areal surface texture feature extraction with the mode aliasing and shape distortion phenomenon remedies. The experimental results show that the mean square error of roughness reconstructed by the proposed filter compared with the benchmarking values is 6.37×10-5, and owing to high flexibility in practice, the devised EBVMD provides a promising solution to achieve a high accuracy and efficiency filter. [ABSTRACT FROM AUTHOR]

The keys to multiple-algorithm fusion methods are the selection of the fusion algorithms and sequence of combination. In this paper, a new multiple-algorithm embedded fusion of infrared polarization and intensity images based on the complementary relation of the algorithms is proposed. First, indexes based on the feature similarities are applied to analyze the complementary relation. Then, in light of the complementary relation, fusion algorithms are selected and the embedded sequence is determined, and a fusion algorithm based on the energy difference degree is used to obtain the low-frequency feature fusion image, and the high-frequency feature fusion images are obtained based on the different combination of the guider filter and non-subsampled shearlet transform (NSST). Finally, the different feature fusion images are combined through two-dimensional variational mode decomposition (2D-VMD). The experiments demonstrate that the proposed method can clearly improve the fusion performance of multiple embedded infrared polarization and intensity images and generate a better image fusion. [ABSTRACT FROM AUTHOR]

Due to their high transmission ratio, high load carrying capacity and small size, planetary gears are widely used in the transmission systems of wind turbines. The planetary gearbox is the core of the transmission system of a wind turbine, but because of its special structure and complex internal and external excitation, the vibration signal spectrum shows strong nonlinearity, asymmetry and time variation, which brings great trouble to planetary gear fault diagnosis. The traditional time-frequency analysis technology is insufficient in the condition monitoring and fault diagnosis of wind turbines. For this reason, we propose a new method of planetary gearbox fault diagnosis based on Compressive sensing, Two-dimensional variational mode decomposition (2D-VMD) and full-vector spectrum technology. Firstly, the nonlinear reconstruction and noise reduction of the signal is carried out by using compressed sensing, and then the signal with multiple degrees of freedom is adaptively decomposed into multiple sets of characteristic scale components by using 2D-VMD. Then, Rényi entropy is used as the optimization index of 2D-VMD analysis performance to extract the effective target intrinsic mode function (IMF) component, reconstruct the dynamics signal in the planetary gearbox, and improve the signal-to-noise ratio. Then, using the full-vector spectrum technique, the homologous information collected by numerous sensors is data layer fused in the spatial domain and the time domain to increase the comprehensiveness and certainty of the fault information. Finally, the Teager–Kaiser energy operator is used to demodulate the potential low-frequency dynamics frequency characteristics from the high-frequency domain and detect the fault characteristic frequency. Furthermore, the correctness and validity of the method are verified by the fault test signal of the planetary gearbox. [ABSTRACT FROM AUTHOR]