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

The invention discloses a method for analyzing harmonics of a power system by adopting Hilbert-Huang transform (HHT), and belongs to the technical field of power quality analysis of power systems. According to the technical scheme, the capable of obtaining integral mode function components of an original signal by adopting HHT comprises the following steps of: obtaining each mode function component of the original signal of a power signal by adopting HHT, judging whether mode mixing exists or not, separating the mode-mixed part of the original signal of the power signal from the original signal, sequentially performing Fourier transform and filtration on an analysis signal, performing HHT on the filtered signal to obtain each mode function component of the analysis signal, and integrating mode function components without mode mixing and each mode function component of the analysis signal to obtain the integral mode function components of the original signal. The method is applied to an electric power sector.

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 power distribution network high-resistance grounding fault recognition method based on a convolutional neural network. According to the method, first, a power distribution network high-resistance grounding fault and a three-phase voltage signal and a zero-sequence voltage signal at the low-voltage side of a main transformer under multiple types of transient disturbance are acquired; second, a local feature scale method is utilized to decompose the signals, equal-bandwidth band-pass filtering is performed on all the voltage signals, a time-frequency matrix is constructed, and a block time-frequency spectrum is obtained; and last, a convolutional neural network algorithm is adopted to perform classification and recognition, and whether the high-resistance groundingfault occurs is judged.

The invention relates to a failure prediction method of a roller bearing based on a partial least squares extreme learning machine. The method herein includes: analyzing feature indexes, such as timedomain, frequency domain and time-frequency domain, providing a feature extraction method based on the combination of half-normal distribution and empirical wavelet denoising to perform failure diagnosis on a roller bearing so as to obtain better denoising effect owing to proximity to original signals; for multi-feature parameters, comprehensively evaluating failure attenuation features of the roller bearing, and providing a method with the combination of residual-modified ISOMAP (isometric feature mapping) nonlinear feature dimension reduction and fuzzy C-means, so that change tendency and sorting precision are improved for the roller bearing in different attenuation stages; based on the extreme learning machine theory, providing a data prediction model based on a partial least squares extreme learning machine, optimizing parameters in the ELM (extreme learning machine), selecting node quantity of an optimal hidden layer and weight value of a connection layer, and selecting a Softmaxactivation function. Therefore, prediction precision is high, calculating time is short, and post-clustering feature value detection is effective. The failure stage of the roller bearing can be precisely predicted via the above steps.

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 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.

A rolling bearing fault diagnosis method based on integral inherent time scale decomposition algorithm comprises the following steps of collecting a vibration signal of a rolling bearing via a displacement sensor, decomposing the collected vibration signal via the integral inherent time scale decomposition algorithm to generate a plurality of rotational components and residual error signals, selecting sensitive rotational components capable of reflecting fault information from all the rotational components, conducting an envelope spectrum analysis on the sensitive rotational components and determining fault types according to envelope spectrum amplitude values corresponding to the fault feature frequency. A modal mixing problem of inherent time scale decomposition algorithm can be solved and great foundation is provided for feature extraction; according to peakedness calculation, rotational components sensitive to the fault are selected; and the fault type is determined via the analysis of the sensitive component envelope spectrum amplitude values corresponding to the fault feature frequency. Rolling bearing faults can be accurately identified and the method is suitable for rolling bearing fault diagnosis.

The invention relates to a noise reduction algorithm for a transient electromagnetic detection signal based on variational mode decomposition. The decomposition process of a goaf strong-interference transient electromagnetic signal is converted into solving of a variational problem. A constraint variation problem is converted into a non-constraint problem by carrying out variation construction onstrong interference transient electromagnetism and introducing a secondary penalty factor and a Lagrange penalty operator, limited modal components are obtained through a multiplication operator alternating direction algorithm, effective separation of signals is achieved, and noise and effective signals are separated. According to the invention, signals can be decomposed in a self-adaptive manner,priori knowledge is not needed, and the actual operation is simpler and more convenient; the variational mode decomposition algorithm is simple, the computer operation time is short, and the storagespace and the calculation time are saved; the modal aliasing problem in empirical mode decomposition is effectively solved, the extracted signals are more accurate, and better noise robustness is shown.

The invention discloses an urban medium-voltage distribution cable partial discharge signal denoising method. In order to suppress periodic narrowband interference, a K-means clustering algorithm is used to classify noisy partial discharge signal spectrums. In order to suppress white noise interference, an improved empirical Wavelet Transform (EWT) algorithm based on an Order Statistic Filter (OSF) is provided, and the method comprises the following steps: firstly, estimating an envelope line on a signal frequency spectrum by using the OSF, reasonably segmenting the signal frequency spectrum, then, carrying out adaptive decomposition on a signal by using the EWT, introducing a kurtosis criterion to select a useful modal component, and finally, carrying out adaptive decomposition on the signal by using the EWT. And finally, reconstructing a partial discharge signal through threshold denoising. In combination with the K-means clustering algorithm and the empirical wavelet transform, the method provided by the invention does not need to modify algorithm parameters, can perform adaptive denoising on white noise and periodic narrow-band interference at the same time, can effectively reserve signal details while having a high denoising signal-to-noise ratio, and is relatively high in algorithm efficiency.

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 self-adaptive signal analysis method based on continuous variable variational mode decomposition. The method is suitable for self-adaptive decomposition, denoising and filtering of multi-channel stable or non-stable signals. According to the method, a decomposition scale and tedious parameter optimization do not need to be set by priori knowledge, and the multi-channel signals can be continuously and adaptively decomposed into intrinsic modes of different time scales only according to the internal time-frequency domain characteristics of the signals. The method has practical value on denoising, adaptive filtering and mode recognition of multi-variable signals in engineering.

The invention provides a short-term power load prediction method based on CEEMD-LSTM-MLR, and the method comprises the steps: 1, obtaining power load data, and carrying out the preprocessing of an obtained data set; 2, decomposing input data into limited IMF components and a residual component through CEEMD, and combining and recombining the components into a high-frequency component and a low-frequency component according to the fluctuation period of each component; step 3, predicting the high-frequency component by using an LSTM neural network, and performing hyper-parameter optimization on the LSTM network by using a Bayesian algorithm; step 4, predicting the low-frequency component by applying MLR; and 5, superposing and reconstructing the prediction results of the components to obtain a final prediction result, and comparing the prediction result with a real load data value. According to the method, the CEEMD decomposition method is adopted, the problem of mode aliasing of a traditional EMD decomposition method and the problem of large EEMD reconstruction error are solved, and the Bayesian optimization algorithm is introduced based on the thought of respective prediction of different frequencies, so that the model prediction precision is further improved.

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.

The invention belongs to the technical field of signal processing, and in particular relates to a mixed signal separation method based on dynamic evolution particle swarm shielding EMD. The method steps are: S1, use a wireless measuring instrument to collect the sound of several devices, and obtain a single signal at time t. Channel mixed signal s(t); S2, use the EMD method to decompose the mixed signal s(t) to obtain the imf signal, and use the imf signal to initialize the amplitude A and frequency f of the shielded signal; S3, through the dynamic evolution based particle swarm shielding The mixed signal separation method of EMD uses the dynamic evolution particle swarm to optimize the particles, and then realizes the optimization of the amplitude A and frequency f of the shielded signal, and then constructs the shielded signal through the optimized amplitude A and frequency f, and analyzes the mixed signal. s(t) is used for separation; S4, the fourth-order cumulant of the signal is calculated, and the effect of signal separation is analyzed. The method can be applied to blind source separation such as mixed sound signal separation, mixed vibration signal separation, and harmonic decomposition.

The invention discloses a vehicle micro-motion signal extraction and classification method based on EEMD. Traditional pattern recognition methods cannot meet the requirements of precise classification in complex environments and complex motion patterns. The present invention first utilizes EEMD to decompose the original signal. Since the micro-Doppler modulation of the wheeled vehicle and the tracked vehicle have obvious differences, in order to further determine which part of the signal each decomposed eigenmode function corresponds to, the Its correlation analysis further verifies the effectiveness of EEMD decomposition. By extracting four features, they are the signal strength of the high frequency band of IMF1, the dispersion between each IMF, the fluctuation degree of the high frequency band of IMF1, and the maximum amplitude of the main part of the IMF2 body. Finally, the support vector machine is used for object classification and recognition. The algorithm provided by the invention improves the recognition rate of the vehicle and is robust to different motion states.

The invention discloses a method for analyzing harmonics of a power system by adopting Hilbert-Huang transform (HHT), and belongs to the technical field of power quality analysis of power systems. According to the technical scheme, the capable of obtaining integral mode function components of an original signal by adopting HHT comprises the following steps of: obtaining each mode function component of the original signal of a power signal by adopting HHT, judging whether mode mixing exists or not, separating the mode-mixed part of the original signal of the power signal from the original signal, sequentially performing Fourier transform and filtration on an analysis signal, performing HHT on the filtered signal to obtain each mode function component of the analysis signal, and integrating mode function components without mode mixing and each mode function component of the analysis signal to obtain the integral mode function components of the original signal. The method is applied to an electric power sector.

The invention belongs to the technical field of network traffic forecasting, and particularly relates to a combined network traffic forecasting method based on ensemble empirical mode decomposition, including: obtaining original traffic data and performing preprocessing; decomposing network traffic into different The IMF component with a single frequency on the time scale; through autocorrelation and partial autocorrelation analysis, determine the stationarity of the IMF component; predict the smooth IMF component with a linear ARMA model; use the nonlinear Elman neural network for the non-stationary IMF component Network prediction; summing the predicted values ​​of each IMF component to obtain the predicted value of network traffic; the invention describes and predicts actual network traffic more accurately and comprehensively, thereby improving prediction accuracy and increasing prediction reliability.

The invention discloses a non-contact video heart rate detection method based on multivariate empirical mode decomposition and joint blind source separation. 2. Each sub-region selects the green channel reference signal or color difference signal as the input signal; 3. Uses multivariate empirical mode decomposition to process the input signal to obtain the input signal eigenmode component data set; 4. Adopts joint blind source separation Process the eigenmode component data set of the input signal to obtain the source signal matrix, and filter out the pulse signal from it; 5. Use the method of frequency spectrum analysis to extract the heart rate from the pulse signal. The invention can obtain video heart rate detection results robustly and accurately, and has important application prospects in daily medical care.

The invention relates to a magnetic resonance sounding (MRS) signal noise filtering field, in particular to a magnetic resonance sounding signal noise filtering method based on variational mode decomposition, which is mainly used for processing power frequency resonance noise and random white noise in the magnetic resonance sounding signals. A 'three-VMD' decomposition approach is provided to better achieve the efficient removal of noise in the noisy MRS signals. MRS signals collected by a magnetic resonance sounding water detector are subjected to band-pass filtering and Fourier transform todetermine the frequencies and the number of the power frequency resonance interference and the single frequency interference included in the MRS signals, the first, second and third VMD decompositionare employed to respectively remove the Gaussian white noise, most of the power frequency and the power frequency being the closest to the signals in the noisy MRS signals, and finally, target MRS signals are extracted and obtained. The magnetic resonance sounding signal noise filtering method can solve the customary modal aliasing problem after a traditional modal decomposition method is employed, and is high in signal-to-noise ratio and high in adaptability compared with the traditional MRS signal denoising method.