Please select the most similar patent number from A, B, C and D. Which number is?
A pipeline leakage detection apparatus and method employing a particle swarm optimization-based variational mode decomposition (PSO-VMD) algorithm. The method comprises: using acoustic emission devices (1, 2, 3) to acquire acoustic emission signals when no leakage occurs and when leakage occurs; acquiring, by means of a PSO-VMD algorithm, a pre-configured scale K and a penalty coefficient α for decomposition of the acoustic emission signal; performing variational mode decomposition (VMD) on the signal to acquire K intrinsic mode function (IMF) components; performing signal reconstruction by using a method employing an energy value, so as to acquire an optimal de-noised observation signal; selecting a time domain feature parameter of the reconstructed signal; and performing leakage detection by means of a support vector machine (SVM) algorithm. The method allows pipeline leakage to be detected in a timely manner, and resolves the issue in which a false alarm rate on a pipeline is high.
A: Disclosed is a fault diagnosis method under the effect of a center frequency convergence trend, comprising (1) acquiring a dynamic signal x(t) of a rotating mechanical device; (2) setting initial decomposition parameters of a variational model; (3) decomposing the dynamic signal x(t) using the variational model with the initial decomposition parameters, under the guidance of the center frequency convergence trend, iterating the signal analysis frequency band to decompose the dynamic signal x(t) to obtain an optimized mode {m 1 ... m n ... m N } and corresponding center frequency {ω 1 ... ω n ... ω N }; (4) searching for a fault-related mode m I , guiding parameter optimization with the center frequency ω I  of the fault-related mode m I , and extracting an optimal target component aa containing fault information; (5) performing envelope analysis of the optimal target component aa, and diagnosing the rotating mechanical device according to the envelope spectrum. In the fault diagnosis method of the present invention, a center frequency convergence trend-guided decomposition method is used to achieve smart decomposition of an original dynamic signal of a diagnostic target device; thus it is possible to adaptively analyze the acquired device dynamic signal, reducing the difficulty for a technician to use the variational modal decomposition method to diagnose mechanical faults.
B: A structural damage identification method based on ensemble empirical mode decomposition and a convolution neural network. The method comprises: selecting an acceleration response signal of a structure as an analysis object; using ensemble empirical mode decomposition technology to decompose the acceleration response signal, so as to obtain a series of intrinsic mode functions (IMFs); selecting an intrinsic mode function (IMF) containing a structural natural vibration frequency for reconstruction; and then using a one-dimensional convolution neural network to perform feature extraction and damage classification on a reconstructed signal, thereby obtaining an identification result of a structural damage position.
C: A pipeline leakage position calculation method based on beam forming. The method comprises: arranging a reference sensor on an outer wall of one end of a target pipeline, and arranging a plurality of auxiliary sensors at intervals on an outer wall of the other end of the target pipeline, wherein the plurality of auxiliary sensors and the reference sensor are arranged in a straight line to form a multi-element linear sensor array; if pipeline leakage occurs, collecting a leakage signal by means of each sensor in the multi-element linear sensor array, and sending the leakage signal to a signal processing terminal; and the signal processing terminal using an auto-spectrum-excluding cross-power spectrum beamforming algorithm to carry out joint calculation on a sound source position where the pipeline leakage occurs and the speed of a sound wave signal being propagated along the pipeline, and then outputting a positioning result.
D: A signal noise filtering method, an apparatus, a storage medium, and a lidar. The method comprises: performing ensemble empirical mode decomposition on an initial difference frequency signal generated by a lidar, and obtaining a component set corresponding to the initial difference frequency signal (S101); acquiring an autocorrelation function energy value corresponding to each noise-containing component in the component set, and acquiring a boundary component corresponding to a largest autocorrelation function energy value among the noise-containing components (S102); performing wavelet threshold denoising on adjacent high order noise-containing components of the boundary component, and obtaining a denoise component corresponding to the adjacent high order noise-containing components (S103); performing signal reconstruction on the basis of a spectrum band region in the frequency spectrum where the initial difference frequency signal is located and on the basis of the denoise component and the boundary component, and obtaining a denoised time domain difference frequency signal (S104). The present method can improve the signal-to-noise ratio of a difference frequency signal, and improve the success rate of active difference frequency frequency extraction.
Answer: C