60 results about "Wigner ville" patented technology

The invention provides equipment failure early-warning system and method. The equipment failure early-warning system has reliable operation, high performance-price ratio and visual interface, can transmit accurate equipment failure early-warning signals in advance and has failure type self-learning function. The equipment failure early-warning method comprises the following steps of: arranging proper types of sensors on proper positions of equipment to collect the parameters, such as bearing temperature, pressure, body vibration waveform, current variation waveform, and the like of the equipment; digitizing signals collected by the sensors by adopting a low-speed data collecting module and a high-speed data collecting module; developing specific computer software to process, store and analyze the various kinds of collected data; realizing the timely time-domain frequency-domain exchange and analysis of the waveforms, such as vibration, electric current, and the like by the software by using the mathematical methods of Fourier transformation, Wigner-Ville distribution, Hilbert-Huang transform, wavelet analysis, and the like; evaluating the various detected parameters by using a scoring method of an established file; and discovering and diagnosing the early exception of the equipment, thereby early warning the equipment failure. The equipment failure early-warning system adopts a modularized framework and comprises four modules of a sensor module, a data collecting module, a data processing module and a self-learning intelligent early-warning platform, the four modules are connected by a data transmission network, and a detection mode can adopt an on-line monitoring mode and a portable point detection mode.

The invention provides a gear failure keyless phase angle domain average computing order analysis method. The method comprises the steps that low-pass phase-protection filtering is conducted on collected accelerated speed signals, and time-frequency distribution of the signals is calculated through smooth pseudo Wigner-Ville distribution; the instantaneous frequency of a gear case rotary shaft is estimated according to a Viterbi optimal path search algorithm, key phase signals are used for estimating a model to conduct point-by-point integration on the instantaneous frequency, and an estimated key phase signal is obtained, calculation order analysis is conducted on the vibration accelerated speed signals through equal angle resampling and the angular domain average technology, and the order spectrum based on instantaneous frequency estimation is obtained. The order spectrogram can effectively reflect the feature information of gear case failures. The gear failure keyless phase angle domain average computing order analysis method integrates the advantages of the smooth pseudo Wigner-Ville distribution, the Viterbi optimal path search algorithm, the angular domain average technology and the calculation order analysis, and can effectively conduct fault diagnosis on a gear case working on the working station of variable speeds.

The invention discloses a multi-scale convolutional neural network based radio signal modulation identification method. The multi-scale convolutional neural network based radio signal modulation identification method comprises the steps of (1) generating a processed radio modulation signal; (2) generating a two-dimensional time-frequency diagram and performing Fourier transform on an instantaneouscorrelation function of the signal to obtain a Wigner-Ville time-frequency distribution diagram of the signals; (3) performing pre-processing on the time-frequency distribution diagram to generate atraining sample set and a test sample set; (4) building a multi-scale convolutional neural network module and training the model; and (5) testing the test set by utilizing the trained network model, calculating the correction rate, obtaining an identification accuracy rate and assessing the network performance. The multi-scale convolutional neural network based radio signal modulation identification method has the advantages of strong universality, no need for manual characteristic extraction and a plenty of priori knowledge, low complexity and accurate and stable classification results, and can be used in the field of signal classification identification technologies.

The invention relates to a radar radiation source identification method based on feature fusion. The method comprises the steps of generating a radar radiation source unintentional modulation signal set; extracting AR model coefficients, Renyi entropy features and spectral kurtosis features; computing smoothed pseudo Wigner-Ville distribution, generating time-frequency images, and carrying out graying and adaptive binarization to obtain adaptive binarized images; extracting pseudo Zernike matrix and Hu matrix features of the images; extracting signal time-frequency image unintentional modulation features through application of an AlexNet convolutional neural network, carrying out normalization, and carrying out feature fusion to obtain fused feature vectors; and inputting the fused featurevectors into a support vector machine, training the support vector machine optimized through particle swarm optimization, and inputting the radar radiation source signal set into a system to finish identifying radar radiation sources. According to the method, the signals are analyzed from a time domain, a frequency domain and a time-frequency domain, various unintentional modulation features areextracted comprehensively, and the problems that the extracted unintentional modulation features are low applicability and reliability and the radiation sources are difficult to identify is solved.

The invention provides a DWT- and Parametric t-SNE-based characteristic extracting method of motor imagery EEG(Electroencephalogram) signals. First, effective time and frequency ranges of EEG characteristics are determined by using a Wigner-Ville distribution and power spectrum; the EEG signals in a specific time and frequency segment is subjected to three-layer discrete wavelet decomposition and statistical characteristic quantity including the average value, the energy average value, the mean square error and the like are calculated and are taken as the time frequency characteristic of the EEG signals; at the same time, a parameterization t-SNE algorithm is utilized for performing non-linear characteristic mapping on said wavelet coefficients and embedded coordinates corresponding to a low-dimensional space are taken as the non-linear characteristic; the two characteristics are standardized and a characteristic vector including both the time frequency information and the non-linear information of the EEG signals in the specific time frequency segment is obtained. According to the invention, EEG characteristics of compactness and completeness are obtained and a method for solving a problem of poor generalization performance of a traditional manifold learning algorithm in pattern classification application through fitting a multilayer forward propagation neural network to nonlinear mapping is proposed, so that accuracy of pattern classification of MI-EEG signals is improved further.

The invention discloses a seismic spectrum imaging method based on deconvolution generalized S transform, which comprises the steps that a generalized S transform spectrum is acquired by performing two-dimensional convolution on Wigner distribution of original signals and a Gaussian window, a transform spectrum is acquired by performing generalized S transform on seismic data, and time-frequency distribution of the original signal can be acquired through deconvolution when the generalized S transform spectrum and the Wigner distribution of a window function are known. The seismic spectrum imaging method combines advantages of generalized S transform and Wigner-Ville distribution, generation of a cross term of the Wigner-Ville distribution is effectively suppressed through a generalized S transform window, and the generalized S transform spectrum is enabled to acquire high time-frequency aggregation at the same time; and deconvolution generalized S transform can adaptively adjust an analysis window along with variations of a frequency component, is applicable to time-frequency analysis for unstable seismic data, and can acquire high time-frequency resolution; and the seismic spectrum imaging method is applied to detecting the oil-gas possibility of a reservoir, thereby being conducive to improving the reservoir prediction accuracy.

The invention provides a nonnegative blind source separation fetal heart sound analysis method and device. The method comprises the following steps: 1) collecting fetal heart sound signals; 2) carrying out pretreatment on the collected fetal heart sound signals, and adopting wavelet transform, which not only can effectively remove noise, but also can keep local singularity of the signals, in the pretreatment process; 3) converting the signals obtained after pretreatment to a time-frequency domain to obtain nonnegative frequency spectrum, the adopted method being: carrying out wigner-ville distribution calculating on the signals obtained after pretreatment and carrying out modulus calculating to obtain magnitude spectrum; 4) carrying out blind source separation by utilizing non-negative matrix factorization and carrying out processing on the fetal heart sound signal amplitude spectrum to obtain nonnegative time-frequency component of the fetal heart sound signal; 5) drawing an envelope curve; and 6) analyzing the envelope, setting dual thresholds to realize envelope segmentation and calculating instantaneous heart rate. The analysis method and device are good in flexibility, can segment the fetal heart sound signal accurately, and are high in instantaneous heart rate identification accuracy.

The invention belongs to application of the non-stationary data analysis field, and particularly relates to a time-frequency spectrum analyzing method for processing earthly natural pulse electromagnetic field data. The time-frequency spectrum analyzing method comprises the following steps: adding normal distribution white noises into original earthly natural pulse electromagnetic field signals, continuously adding the normal distribution white noises and performing EEMD (ensemble empirical mode decomposition) on the original earthly natural pulse electromagnetic field signals until a group of IMF (intrinsic mode function) components with a single frequency are obtained, performing WVD (Wigner-Ville distribution) decomposition on each IMF component, and summing WVD decomposition results. By the time-frequency spectrum analyzing method for processing the earthly natural pulse electromagnetic field data, relatively obvious distribution abnormality of a time-frequency energy spectrum before an earthquake can be acquired, so that time-frequency characteristics of seismogenic information can be effectively studied on the basis of earth natural pulse electromagnetic field analysis.

The invention discloses a traveling wave time-frequency analysis method based on variational mode decomposition and Wigner-ville distribution. The method includes the following steps that: fault traveling wave signals are detected, and Karebauer phase-mode transformation is performed on three-phase voltage traveling wave signals, so that a traveling wave aerial mode component is obtained; variational mode decomposition is performed on the traveling wave aerial mode component, so that K intrinsic mode components are generated; Wigner-Ville analysis is performed on each intrinsic mode component;and the Wigner-Ville distribution of each intrinsic mode component is linearly superimposed, so that the time-frequency domain distribution of original traveling wave aerial mode signals is obtained.With the method of the invention adopted, the interference of cross terms in Wigner-Ville distribution can be effectively suppressed; a good noise suppression effect enabling good VMD (Variational Mode Decomposition) is preserved; time-frequency resolution enabling high Wigner-Ville distribution and good time-frequency aggregation are preserved; traveling wave time-frequency domain information characteristics are truly and accurately represented; and fault traveling waves can be completely observed. The method is of important theoretical and practical significance for the practical application of fault traveling wave protection and positioning.

The embodiment of the invention discloses a rotational speed estimation method and device based on gear meshing vibration, which are used for enhancing the anti-noise capability and improving the precision of the estimated rotational speed curve. The rotational speed estimation method comprises the following steps: acquiring a gear meshing vibration signal; calculating the Wigner-Ville distribution and the wavelet scale spectrum of the gear meshing vibration signal; achieving time-frequency fusion of the Wigner-Ville distribution and the wavelet scale spectrum to obtain the time-frequency fusion distribution; according to the time-frequency fusion distribution, determining the gear meshing frequency curve; according to the gear meshing frequency curve and the number of gears of a driving wheel, determining the rotational speed curve of the driving wheel.

The invention provides a method for extracting a fluid factor. The method comprises the following steps of: detecting at least one channel seismic data in a work area; setting an initial value of a fractional order number used for fractional order Fourier transform; calculating the fractional order Fourier transform of one channel seismic data in the at least one channel seismic data according tothe fractional order number; calculating an analytic signal of the fractional order Fourier transform, and Wigner-Ville distribution with suppressed cross terms of the analytic signal; calculating Radon transform of a band window of the Wigner-Ville distribution with the suppressed cross terms; reconstructing a time frequency distribution map according to the Radon transform of the band window; and extracting the fluid factor from the time frequency distribution map. According to the method for extracting the fluid factor, the fluid factor can be accurately extracted from seismic waves even though the seismic waves have a dispersion effect.

The invention discloses a method and a system for measuring a power angle of an electric power circuit of a distribution network, which belong to the technical field of power electronics. The system for measuring the power angle is positioned at a power substation end; a main transformer is connected with main station equipment through a used transformer; and a user transformer at a low-voltage user end is connected with a power angle measurement end. The method comprises the following steps of: controlling the conduction of a silicon controlled rectifier by a signlechip on a low voltage side of the used transformer inside a power substation, and generating a voltage distorted signal when short circuit happens; after a voltage waveform with the distorted signal is received by the user end, extracting a single distorted signal from a network voltage waveform by time domain differential technology, and accumulating a plurality of distorted signals so as to form a synthesized signal; simultaneously, performing time-frequency analysis processing on the synthesized signal by combining Wigner-Ville distribution with short-time Fourier transform, determining the time difference between the distorted signal and a local voltage zero crossing point according to the time distribution condition of power parameters of the synthesized signal, and combining a connection group relation of the transformer so to obtain the power angle of the circuit. The method is low in cost, is easy to operate, is not influenced by the electric power network structure, and is widely used.

The invention discloses a character guided wave based butt-weld defect detection system and a detection method thereof. The detection system is composed of a character guided wave transceiving host, a character guided wave sensor and an upper computer; the character guided wave sensor is horizontally arranged on the upper end face of a detected welding line along the direction of the detected welding line, a signal input end and a signal output end of a sensor of the character guided wave transceiving host are respectively connected with the character guided wave sensor, a communication serial port is connected with the upper computer, and data processing analysis software and a database are installed on the upper computer. The detection method includes: adopting a ratio modal analysis method to separate modal wave packets, and selecting specific modals to have defects positioned according to a pulse-echo method; extracting energy information by the aid of a frequency diagram with smoothness being Wigner-Ville and evaluating sizes of the defects; extracting wave characters and inquiring the waveform comparison database to judge types of the defects. By the arrangement, in-service detection can be realized, and the system is high in detection efficiency, low in cost, portable, especially suitable for rapid and efficient detection for long-distance welding lines of large equipment structures and high in automation degree.

The invention provides a method and a system for monitoring low-frequency oscillation of a turbo generator set speed control system. The method comprises the following steps: an original power signal and an original valve opening signal on the side of a turbo generator set speed control system are acquired, and down-sampling rate processing is carried out on the original power signal and the original valve opening signal respectively to obtain a power signal and a valve opening signal; Wigner-Ville distribution transform is performed on the power signal and the valve opening signal respectively to obtain a first time-frequency distribution characteristic of energy of the power signal and a second time-frequency distribution characteristic of energy of the valve opening signal; and low-frequency oscillation of the turbo generator set speed control system is monitored according to the first time-frequency distribution characteristic and the second time-frequency distribution characteristic. By adopting the scheme of the invention, effective real-time monitoring of low-frequency oscillation of the turbo generator set speed control system is realized, the economic efficiency can be improved, and the grid security can be ensured.

The invention discloses an urban rail train wheel flat fault detection method. The method is on the basis of an improved Wigner-Ville distribution time frequency analysis method, and comprises the following steps of: acquiring signals on the spot, and carrying out adaptive denoising on vibration signals; carrying out continuous wavelet transform on the vibration signals, and linearly dividing a wavelet spectrogram into time frequency units so as to satisfy dimensionality of WVD; extracting a wavelet coefficient, analyzing signal features and estimating a threshold value; carrying out IWVD processing on the vibration signals, and carrying out wheel state discrimination according to a time frequency graph and a time frequency amplitude graph of the signals; if cross interference items exceed a component number N of the vibration signals, returning to re-calculate the threshold value and continuing to carry out processing. The method is on the basis of improved Wigner-Ville time frequency distribution, so that the detection result is obvious and the applicability is high.

The invention discloses a signal time frequency characteristic image generation method. The method comprises the following steps of: acquiring a time domain waveform signal; carrying out Wigner-Ville distribution transform (WVD) and wavelet transform (WT) on the time domain waveform signal respectively to obtain two time frequency matrices; carrying out normalization processing on each time frequency value in the two time frequency matrices; merging the two time frequency matrices which are subjected to the normalization processing into one time frequency matrix; carrying out inverse normalization processing on the merged time frequency matrix; according to the time frequency matrix which is subjected to the inverse normalization processing, generating a signal time frequency characteristic image. The invention also discloses a signal time frequency characteristic image generation device. A generated signal time frequency characteristic image by using the method or the device has a higher time frequency resolution, and an introduced cross term when generating the image can be eliminated.

The invention discloses a novel diamond press anvil crack online detection method. By using a backtracking search optimization algorithm (BSA), a support vector machine model parameter is optimized and selected to avoid blindness of parameter selection; in the application of a K-SPWVD method, a template matching method and K-S inspection are utilized to avoid complicated time-frequency analysis of each signal to be detected and enhance the efficiency of the algorithm; and good time-frequency analysis performance of smooth pseudo Wigner-Ville distribution is also sufficiently unitized. The novel diamond press anvil crack online detection method disclosed by the invention has high accuracy and high computing speed and is beneficial for online detection of diamond press anvil cracks.

The invention discloses an improved radar signal time-frequency analysis method, which comprises the following steps: S1: performing signal decomposition operation based on an improved empirical modedecomposition algorithm on a signal to obtain a series of mutually orthogonal intrinsic mode function components and a residual component Res(t), wherein the t is signal duration; S2: screening the obtained intrinsic mode function components, and removing invalid intrinsic mode function components to obtain valid intrinsic mode function components; S3: performing Hilbert transformation on each obtained intrinsic mode function component to convert each basic mode component into an analytic signal; and S4: respectively performing time-frequency analysis based on a rearrangement smooth pseudo Wigner-Ville distribution algorithm on the valid intrinsic modal function components to obtain an analysis result after the time-frequency analysis processing of the rearrangement smooth pseudo Wigner-Ville distribution algorithm. The improved radar signal time-frequency analysis method of the invention solves the problems of low parameter estimation precision for single-component nonlinear signals and low parameter separation and estimation precision for multi-component nonlinear signals in the conventional method.

The invention discloses a robust communication signal modulation and recognition method and relates to communication signal modulation and recognition methods. The method aims at solving the problem that in order to guarantee validity within a large SNR range, a plurality of recognizers need to be trained through a traditional ARM algorithm, and in other words, the recognizers need to be trained individually for different SNR environments at a training stage and accordingly the workload is huge. The method includes the steps that Wigner-Ville conversion is performed on a communication signal sample s(t) to obtain WVD of the s(t); second-order three-dimensional autocorrelation characteristics are extracted, and a second-order three-dimensional autocorrelation characteristic set is established; selection is performed on the second-order three-dimensional autocorrelation characteristics to form a robust feature set; a first-class support vector unit is established through training, and an output function Yi(x) of the first-class support vector unit is calculated; the possibilities that a communication signal sample to be recognized sx(t) belongs to the various modulation modes included in the communication signal sample s(t) are calculated, and a modulation category with the largest possibility is selected as the final modulation and recognition result. The method is suitable for communication signal modulation and recognition.

The invention belongs to the technical field of hydrogen production by water electrolysis, in particular relates to a method for diagnosing the fault of a rectifying cabinet on the basis of wavelet transformation and Wigner-Ville distribution. A decomposing layer number n of wavelets is chosen according to a frequency band where a fault signal among signals is positioned; db1 and db3 are used as generating functions of the wavelets, multiresolution decomposition of n to 1 layers are respectively carried out so as to decompose the original fault signal into n sequences with each of which having only one component; each component is analyzed according to the Wigner-Ville distribution, the Wigner-Ville distribution of all the sequences are combined to form the time frequency of the original signal, and the multiple dimensioned on-line analytical method of the signal is realized. The method can greatly enhance the accuracy for diagnosing the fault, the reliability and the maintenance level of the prior rectifying cabinet for producing hydrogen by water electrolysis so as to ensure that equipment for producing hydrogen by water electrolysis safely and reliably works.

The invention discloses a VMD and Wigner-Ville based high pressure diaphragm pump check valve fault diagnosis method. The method includes: using an acceleration sensor to acquire vibration signals ofa high-pressure diaphragm pump check valve in a fault state and a normal state, and then adopting a VMD method to decompose the acquired signals in each state, and then selecting IMF components basedon a center frequency observation method for k-modal components of a fault vibration signal and a normal vibration signal; calculating the Wigner-Ville distribution of each selected IMF component; linearly superimposing the Wigner-Ville distribution results of each modal component to obtain the Wigner-Ville distribution of the fault and normal vibration signals; acquiring the vibration signals ofthe high pressure diaphragm pump check valve, comparing the vibration signals with the Wigner-Ville distribution of the fault vibration signal and the normal vibration signal, and extracting fault features; and diagnosing the fault. The method realizes the comprehensive and effective characterization of the state information of the check valve, and effectively achieves fault diagnosis of the high-pressure diaphragm pump check valve.

The invention discloses a ground moving target detection method of a BFSAR, and aims at solving problems of ground moving targets under configuration of the BFSAR. The method comprises the steps thatS1) original echo signals of different channels are pre-filtered; S2) Keystone transformation is carried out on filtering results of the different channels in the S1; S3) ground still-life clutters are inhibited by time-division adaption; and S4) an improved Wigner-Ville distribution method is used to accumulate target energy coherently. A de-inclination pre-filter and Keystone transformation areused to inhibit Doppler fuzziness and correct migration of a distance unit, influence of migration of the Doppler unit is eliminated by time-division adaptive cancellation, and the ground still-life clutters are inhibited effectively; and the improved Wigner-Ville distribution method is used accumulate the target energy coherently, and the signal to clutter/noise ratio is improved.

Traditional radar signal classification methods usually manually analyze and extract various low-intercept probability radar signal features and then carry out classification by utilizing the extracted features, so that the classification correctness in practical application is relatively low. The invention discloses a pseudo Wigner-Ville distribution analysis and novel convolutional neural network model combined method for classifying low-intercept probability radar signals. The method comprises the specific steps of: 1, intercepting an LPI radar signal; 2, carrying out Wigner-Ville distribution processing on the intercepted radar signal to obtain a radar signal image; 3, normalizing the processed radar signal image; 4, classifying the radar signal image on the basis of a novel convolutional neural network; and 5, outputting an LPI radar signal classification result. The radar signal classification method provided by the invention is capable of automatically extracting radar signal features, along with the increase of collected radar signal data, the classification correctness is enhanced, so that the method has self-adaptation ability and has important significance for enhancingthe electronic countermeasure ability of the country.

The invention belongs to the technical field of radar signal processing in a non-Gaussian clutter environment, and discloses a radar signal identification method based on generalized variational modedecomposition combined with a support vector machine, which comprises the following steps: carrying out generalized variational mode decomposition on a received radar radiation source signal to obtainan intrinsic mode component IMF; computing smooth pseudo Wigner-Ville time-frequency distribution matrix for each intrinsic mode component, and extracting Renyi entropy features of time-frequency distribution to construct a feature vector T; and finally, carrying out classification and identification by utilizing a support vector machine. When the generalized signal-to-noise ratio is larger than5dB, the identification rate of various signals reaches 65% or above, especially when the generalized signal-to-noise ratio is larger than 10dB, the identification rate of various signals reaches 90%or above, and therefore the identification effect of the method is good.

The invention relates to a two-phase coded signal parameter estimation method based on smoothing pseudo-Wigner-Ville distribution, and mainly solves the problem that the existing method cannot consider both low signal-to-noise ratio and low computational burden. The method comprises the implementation steps that 1) two-phase coded signals of 13-bit barker code sequence coding are generated; 2) FFT transformation is performed on the two-phase coded signals so that the power spectrum is solved; 3) smoothing is performed on the power spectrum and the signal carrier frequency is estimated according to 3dB bandwidth power spectrum centroid of the smoothed power spectrum; 4) smoothing pseudo-Wigner-Ville distribution transformation is performed on the two-phase coded signals so that the amplitude sequence of the carrier frequency can be obtained; 5) the signal code element width is obtained by searching amplitude sequence minimum negative peak spacing, and the number of sub-codes included in the adjacent negative peaks is computed; and 6) estimation of the code element sequence is obtained according to the position of the negative peaks and the number of the sub-codes. The estimate precision for the parameters of the two-phase coded signals can be enhanced under the condition of low signal-to-noise ratio so that the computational burden can be reduced and the method can be used for accurate detection of the target.