38results about How to "Solve the problem of modal aliasing" patented technology

The invention belongs to the technical field of network traffic prediction, and particularly relates to a combined network traffic prediction method based on ensemble empirical mode decomposition, which comprises the following steps: obtaining original traffic data and preprocessing the original traffic data; decomposing the network flow into IMF components with single frequency on different timescales through ensemble empirical mode decomposition; determining the stationarity of the IMF component sequence through autocorrelation and partial autocorrelation analysis; predicting the stable IMFcomponent by using a linear ARMA model; predicting the non-stationary IMF component by using a nonlinear Elman neural network; summing the predicted values of the IMF component sequences to obtain apredicted value of the network traffic; According to the method, the actual network flow is described and predicted more accurately and comprehensively, so that the prediction precision is improved, and the prediction reliability is improved.

The invention relates to a high speed railway rail corrugation acoustic diagnosis method based on an IMF (Intrinsic Mode Function) energy ratio, and belongs to the technical field of high speed railway vibration noise. The method comprises the following steps that: (1) testing rail roughness; (2) carrying out ensemble empirical mode decomposition; and (3) obtaining an IMF energy ratio: according to the energy ratio of an IMF signal corresponding to fault characteristic frequency, carrying out rail corrugation fault identification, screening to obtain an IMF component corresponding to the railcorrugation, and obtaining a Hilbert marginal spectrum and instantaneous frequency through HHT (Hilbert-Huang Transform). By use of the high speed railway rail corrugation acoustic diagnosis method based on the IMF energy ratio, a direct method is adopted on an operation line to actually measure the rail roughness characteristics of a corrugation-free zone, the IMF energy ratio obtained after an acoustic signal is subjected to EEMD (Ensemble Empirical Mode Decomposition) is subjected to component screening, and IMF energy ratio distortion characteristics are used for carrying out rail corrugation identification. Compared with theoretical acoustic frequency corresponding to the rail roughness actually measured by the direct method, the invention puts forward an efficient high speed railwayrail corrugation acoustic diagnosis strategy.

The invention discloses an algorithm for carrying out feature extraction, classification and identification on burial area invasion vibration signals. The algorithm is suitable for multi-grating sensing and detection systems, and comprises the following steps of: acquiring vibration signals through a multi-grating sensor network; carrying out decomposition by utilizing an EEMD algorithm so as to obtain an IMF component; calculating an EEMD energy entropy; eliminating interferences of un-artificial signals; carrying out feature extraction; and finally carrying out classification, identification and early warning on invasion signals by adopting a support vector machine which is optimized through a particle swarm algorithm. According to the algorithm disclosed by the invention, the alarm correctness is improved, the false alarm rate is reduced and a practical value is provided.

The invention discloses a power system sub/super-synchronous harmonic detection method comprising the following steps: extracting and separating power-frequency components; carrying out grouping according to modal aliasing conditions; extracting a grouping signal with the highest-frequency harmonic component, and judging whether modal aliasing occurs; if modal aliasing occurs, carrying out frequency shift modulation and empirical mode decomposition to get an intrinsic mode function corresponding to sub/super-synchronous harmonics contained in the group; carrying out amplitude phase and frequency compensation; carrying out Hilbert transform to get the instantaneous frequency and amplitude of the sub/super-synchronous harmonics contained in the group; separating the sub/super-synchronous harmonics contained in the group; and repeating the steps until the sub/super-synchronous harmonics contained in all the groups are separated. The advantages are as follows: based on a frequency shift method of signal modulation, the signal decomposition range of empirical mode decomposition is extended by increasing the frequency ratio of two sub/super-synchronous harmonic components; and the method is applicable to real-time accurate detection of sub/super-synchronous harmonics in a power system.

The invention provides an electrocardiosignal denoising method based on improved EMD (Empirical Mode Decomposition) and threshold method fusion, and belongs to the technical field of signal wave filtering. The mode mixing problem is solved by adding white noise with different weight coefficients. The end point problem is solved by a method of a least squares support vector machine. Signal upper and lower envelope lines are constructed by a conformal spline interpolation method; and cubic spline interpolation with second-order approximation accuracy, few segments and small calculation quantityis constructed by a conformal segmentation method. The method can be used for inhibiting the envelope fitting overshoot/undershoot problem; IMF (Intrinsic Mode Function) component sieve termination criterion is provided by the decomposed IMF orthogonality and energy properties; the orthogonality and completeness of the EMD are ensured; the amount of noise contained in the sieved IMF signals is judged through a mutual information principle, whether the filtering processing is needed or not is determined, and the high speed of an EMD algorithm is accelerated; a threshold function is improved;the threshold function combines with the advantages of soft-hard thresholds; and the filtering processing is performed on IMF containing the noise.

The invention discloses a reconstruction and extraction method for diffraction echo signals in laser ultrasonic defect detection. The method comprises the steps of firstly inputting a to-be-processedlaser ultrasonic defect detection diffraction echo signal, and sequentially carrying out subtraction and white noise adding to generate a noise-added signal; decomposing the noise-added signal by adopting an EMD (Empirical Mode Decomposition) method to generate a plurality of IMF components, sorting the IMF components according to the frequency, calculating the dispersion degree of each IMF component, and selecting the corresponding IMF component to reconstruct according to the dispersion degree distribution and the IMF component relationship to obtain a reconstructed signal; denoising the reconstructed signal in an exponential weighted average mode, extracting extreme points of the denoised reconstructed signal, and selecting and outputting a certain number of maximum extreme points at intervals of a certain length. Useful signals are prevented from being lost; the decomposition effect of EMD is improved; useful signals are prevented from being submerged in incident signals; useful signals containing defect information are effectively reserved, high-frequency noise signals from the environment are abandoned, and the purposes of accurate output of the useful signals and visual judgment of defects are achieved.

The invention discloses an EEMD-based vehicle micro-tremor signal extraction and classification method. A conventional mode identification method cannot satisfy accurate classification under the conditions of complex environments and complex motion modes. First of all, original signals are decomposed by use of EEMD, since an obvious difference exists in micro Doppler modulation between a wheeled vehicle and a tracked vehicle, for the purpose of further determining signals corresponding to each intrinsic mode function after decomposition, correlation analysis is carried out, and the effectiveness of the EEMD is also further verified. Four features, which are respectively signal intensity of the high frequency band of IMF1, discretivity between the IMFs, a fluctuation degree of the high frequency band of the IMF1, and an amplitude maximum value of the main body part of IMF2, are extracted, and finally, object classification identification is carried out by use of a support vector machine. The algorithm provided by the invention improves the vehicle identification rate and has robustness for different motion states.

The invention provides a method and a device for detecting motor bearing failure. The method comprises the following steps of: collecting a vibration signal of the motor bearing; decomposing the collected vibration signal into a plurality of modal signal components by using a variational mode decomposition method; determining a sample entropy value of the vibration signal based on the decomposed plurality of modal signal components; and determining the sample entropy value of the vibration signal as a fault characteristic of the motor bearing to determine a fault type of the motor bearing. Thedevice comprises a signal collection unit, a signal decomposition unit, an entropy value determination unit, and a failure determination unit, wherein the signal collection unit collects the vibration signal of the motor bearing; the signal decomposition unit decomposes the collected vibration signal into a plurality of modal signal components by using a variational mode decomposition method; theentropy value determination unit determines the sample entropy value of the vibration signal based on the decomposed plurality of modal signal components; and the fault determination unit determinesthe sample entropy value of the vibration signal as the fault characteristic of the motor bearing to determine the fault type of the motor bearing.

The invention discloses a cable insulation condition monitoring method and system based on QGA-VMD, and belongs to the technical field of cable insulation condition monitoring. The technical problem to be solved is to provide the improvement of the cable insulation condition monitoring method based on the QGA-VMD. According to the technical scheme, the method comprises the following steps: sampling original voltage and original current of a cable through a sampling circuit, and carrying out preliminary filtering processing on the collected original voltage and current data; a penalty factor and a decomposition mode number in the variational mode decomposition algorithm are optimized by using a quantum genetic algorithm, and an optimized QGA-VMD algorithm is obtained; performing signal decomposition on the obtained voltage and current signals through an optimized QGA-VMD algorithm, and extracting a voltage fundamental component and a current fundamental component of which the frequencies are close to the power frequency; calculating the extracted voltage fundamental component and current fundamental component by using a sine wave parameter method to obtain a dielectric loss factor; the method is applied to cable insulation monitoring.

The invention discloses a multi-mode signal resolving and separating method and device, equipment and a storage medium. The method comprises the following steps that: decomposing a multi-mode frequency measurement signal to obtain a first mode component signal, wherein the multi-mode frequency measurement signal is a first time domain signal; judging whether the first mode component signal is a single-mode component signal or not, if the first mode component signal is the single-mode component signal, deciding that the first mode component signal is correctly decomposed, and on the basis of the first time domain signal and the first mode component signal, generating a to-be-decomposed time domain signal for next decomposition; if the first mode component signal is not the single-mode component signal, carrying out band-pass filtering processing on the first modal component signal subjected to mode mixing to obtain the updated single-mode component signal, and generating the to-be-decomposed time domain signal used for next decomposition; and judging whether the to-be-decomposed time domain signal meets a termination condition for mode component decomposition or not, and if the to-be-decomposed time domain signal does not meet the termination condition for mode component decomposition, repeating the above steps for continuous decomposition until the complete decomposition of each mode component signal is finished. By use of the method, the problem that a single-frequency mode component can not be accurately separated in the multi-mode frequency measurement signal can be solved.

The invention discloses a terahertz time domain signal noise reduction method, an image reconstruction method and a system, and belongs to the field of terahertz signal processing and image reconstruction. Noise-containing modes obtained through empirical mode decomposition are screened through a threshold value filtering method to obtain a virtual noise channel, then noise-containing signals are separated into noise and noise-reduced signals through independent component analysis, the mode aliasing problem existing in empirical mode decomposition is well solved, and compared with an existing method, the method has the advantages that the noise-containing modes obtained through empirical mode decomposition are screened, and the noise-containing signals are obtained. And the signal-to-noise ratio of the signal after noise reduction is greatly improved. When the method is further used for imaging, compared with an existing imaging method, the imaging signal-to-noise ratio and the contrast ratio are remarkably improved.

The invention discloses a mechanical fault diagnosis method for a transformer on-load tap-changer. The method comprises the following steps of measuring multi-channel vibration signals in an OTLC switching process and carrying out EEMD decomposition and Hilbert transform to obtain a Hilbert marginal spectrum; constructing a Volterra model to obtain a coefficient vector; computing a singular value of a coefficient vector matrix; and identifying a mechanical state of the OTLC through a DAG-SVM. According to the method, improved Hilbert-Huang transform and the Volterra model of a chaotic time series are introduced, the phenomenon of modal aliasing in a signal decomposition process is inhibited, high-resolution characteristic parameters of non-stationary vibration signals can be quickly obtained, and the adaptability, the characteristic resolution and the identification efficiency of the mechanical fault diagnosis of the OTLC are improved.