129 results about "Hilbert–Huang transform" patented technology

The invention discloses a new method for performing fault line selection by using the characteristic of a non-power frequency transient state fault component for extracting single-phase fault zero-sequence current by using Hilbert-Huang transform (HHT). The method comprises the following steps: performing EMD decomposition on fault zero-sequence current of each feeder line by using the HHT to acquire an IMF component of the zero-sequence current of each feeder line; constructing a resolving signal according to Hilbert transform, solving instantaneous amplitude and frequency of the zero-sequence current of each feeder line, and eliminating a power frequency component in the instantaneous frequency of the zero-sequence current of each feeder line to acquire a non-power frequency fault component; and forming a line selection criterion according to the characteristics that the amplitude of the non-power frequency fault component of the fault line is greater than that of the non-fault line and the polarity of the fault line is opposite to that of the non-fault line. The line selection method is not affected by an arc suppression coil, and is applicable to non-grounded, arc suppression coil grounded and high-resistor grounded systems, overhead lines or cable lines and cable overhead mixed lines; and the line selection result shows that the method applied to low-current single-phase grounding fault line selection is accurate and reliable.

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 relates to a stable vision-induced brain-computer interface-based robot control method. The method comprises the steps of firstly preprocessing an electroencephalogram signal obtained by actual collection with bandpass filtering; secondly, performing fast independent component analysis on the electroencephalogram signal obtained by preprocessing to obtain independent components; then using a Hilbert-Huang transform to dissolve the independent components to obtain an intrinsic mode function; performing spectrum analysis on the intrinsic mode function to obtain the needed features; finally using a threshold value judging method to classify the extracted features, and translating the classifying result into signals capable of being identified by a robot, thus realizing the real-time control on the robot. The robot control method is based on a stable vision-induced brain-computer interface, is high in transmission rate and is simple in equipment and device. The independent component analysis and Hilbert-Huang transform are combined in the feature extracting process, so the feature extracting is more effective. The limb-free action control on the motion of the robot is realized, and the severely paralyzed disabled with normal brain functions can control the robot to assist the disabled in normal living.

An audio peak limiter in which the lower sideband of the modulation distortion spectrum is canceled. The peak limiter is realized by multiplying the input signal by a control signal. The Hilbert transform of the control signal is multiplied by the Hilbert transform of the input signal and the outputs of the two multipliers are added. This cancels the lower sideband of the modulation process. Various filters can be added to eliminate aliasing in digital realizations, to band-limit the output signal, and to control peak overshoot.

An apparatus, computer program product and method of analyzing non-stationary time varying phenomena. A representation of a non-stationary time varying phenomenon is recursively sifted using Empirical Mode Decomposition (EMD) to extract intrinsic mode functions (IMFs). The representation is filtered to extract intrinsic trends by combining a number of IMFs. The intrinsic trend is inherent in the data and identifies an IMF indicating the variability of the phenomena. The trend also may be used to detrend the data.

The invention discloses a transformer fault classification and identification method based on a vibration analysis method. The method comprises the following steps: S1) selecting a transformer test object, and collecting transformer vibration signals under different states as sample data; S2) calculating the sample data by utilizing a Hilbert-Huang transform EEMD (ensemble empirical mode decomposition) method to obtain an intrinsic mode function; S3) extracting characteristic vector V in each intrinsic mode function component; S4) carrying out dimensionality reduction on the characteristic vector through a principal component analysis method, coordinates being projected to a two-dimensional image; S5) carrying out classification on the sample data through a K neighbor method; S6) calculating the distance between a test sample and an original sample through a distance formula; S7) carrying out mode recognition; and S8) outputting corresponding transformer fault type in mode recognition. Operation state of a transformer can be judged visually and effectively.

The invention provides a micro moving target feature extracting method based on micro Doppler effect, which comprises the steps that HHT (Hilbert-Huang transform) is introduced into micro moving target feature extracting, an HHT algorithm based on the down sampling EMD (empirical mode decomposition) is provided by aiming at the problem of the modal mixing of the feature extracting of the HHT, and procedures of resolving, summing and averaging on the noisy EMD are performed by the multiple groups of data obtained by performing the down sampling on original signals, thus effectively solving the mixing problem of spectrogram modes in the vibration target feature extracting of the HHT, inhibiting the noise of the original signals, improving the signal to noise ratio, reducing the EMD operating complexity of the multiple groups of data, greatly reducing the operating amount, improving the operating speed and achieving a better micro Doppler feature extracting effect. The micro Doppler feature extracting model based on the improved HHT is provided by integrating the advantages of the traditional time frequency analysis method and the improved HHT algorithm, a spectrogram peak value estimation method is added into the model, the resolution in the traditional time frequency spectrogram is improved to be used as an assisting method for the HHT feature extracting, and the requirements on accuracy and practicability of the extracted vibration target feature are achieved.

The invention discloses a demagnetization fault diagnosis and fault mode identification method for a PMSM permanent magnet. On the basis of a algebraic method, on-line identification of PMSM electromagnetic parameters including a permanent magnet flux linkage is realized and a permanent magnet flux linkage identification result is used as a basis of a qualitative diagnosis of a permanent magnet demagnetization fault; with introduction of Hilbert-Huang transform, a weak fault feature signal for representing local demagnetization of a permanent magnet is extracted, thereby realizing a local demagnetization fault diagnosis of a PMSM permanent magnet; and on the basis of combination with the algebraic-method-based qualitative diagnosis of the demagnetization fault of the PMSM permanent magnet, identification of uniform demagnetization and local demagnetization fault modes of the PMSM permanent magnet is realized on stable and non-stable system operation conditions. Therefore, an integration solution can be provided for demagnetization fault diagnosis and fault mode identification of the PMSM permanent magnet; and the effectiveness and reliability are high.

The invention discloses a transformer fault detection method based on an SOM (Self Organizing Map) neural network. The method comprises the following steps: S100: selecting a transformer as a testing object, and acquiring vibration signals of the transformer in different states as sample data; S200: decomposing and extracting a characteristic vector by utilizing ensemble empirical mode decomposition in Hilbert-Huang transform; S300: inputting the characteristic vector into the SOM neural network; S400: calculating a distance between a weight of a mapping layer and an input vector; S500: adjusting weights of an efferent neuron and an adjacent neuron; S600: judging whether pre-set conditions are met or not, and finishing SOM neural network training to obtain a testing sample; and S700: inputting the testing sample, and outputting the transformer fault type corresponding to the testing sample according to the network, thereby realizing the technical effect of online detection of the transformer.

An internal impedance measurement system of switch power-supply EMI noise source based on Hibert transform and current probe comprises line-impedance stable network, common mode/difference mode impedance test module, signal generator and frequency spectrum analyzer. It is featured as inputting power supply wire directly to impedance stable network then inputting to switch power supply and load for forming a complete power supply loop through common mode/different mode impedance test module. The method for measuring said internal impedance is also disclosed.

A method for coding in frequency, module and phase a digital representation, in the space field, of a ring-shaped element, including the steps of: applying to any point of the element a polar conversion at constant angle, whereby the element is unfolded in rectangular form; transferring, to the frequency field, any point of the converted rectangular shape by means of a Fourier transform; filtering the discrete data resulting from the transfer by means of at least one real, bidimensional, band-pass filter, oriented along the phase axis; applying a Hilbert transform to the filtering results; applying an inverse Fourier transform to the results of the Hilbert transform; and extracting phase and module information in the space field.

The invention relates to a submersible motor rotor broken bar fault recognition method based on HHT (Hilbert-Huang transform), belongs to the technical field of motor broken bar fault recognition and aims to solve the problem that the rotor broken bar fault of the existing submersible motors cannot be detected online, which causes severe burning loss of the motors. The method includes: acquiring three-phase stator current of a submersible motor; acquiring a 0-3.3V voltage signal as an to-be-recognized original stator current signal, processing to obtain stator current amplitude X(t), acquiring an analyzing signal Z(t), performing Fourier transform to the analyzing signal Z(t) to acquire a stator current signal amplitude frequency spectrum W(omega), and acquiring side frequency component in motor stator current through the stator current signal amplitude frequency spectrum W(omega); and comparing amplitude of the acquired side frequency component with rated amplitude, and judging that rotor broken bar fault occurs when the amplitude of the side frequency component is larger than the rated amplitude. The method is applicable to recognition of submersible motor rotor broken bar fault.

The invention relates to an ultra-high-voltage direct-current transmission line single-end protective method, and belongs to the technical field of high-voltage direct-current transmission system replay protection. The method includes the steps that firstly, a data acquisition device acquires the fault current data in a time window of 5 ms after a fault current travelling wave head wave head reaches, and a 1 model component is solved; then 1 model current which operates normally before fault is subtracted from a 1 model component current signal, the fault current variable quantity is obtained, summation operation is conducted on the fault current variable quantity, and whether the signal comes from the exterior of a rectification side region or from the exterior of a circuit or the exterior of a contravariant side region is judged according to the summation operation; Hilbert-Huang transform is conducted on the fault current signal from the exterior of the circuit or the exterior of the contravariant side region, the instantaneous frequency maximum value of the signal is obtained, the obtained instantaneous frequency maximum value is compared with the preset threshold value to judge whether the circuit or the exterior of the contravariant side region breaks down. By means of the ultra-high-voltage direct-current transmission line single-end protective method, the full-line protection of an ultra-high-voltage direct-current transmission line can be achieved, and the good speed can be guaranteed.

The invention provides a seismic cycle analysis method, comprising the steps of: processing Hilbert-Huang transform for the reflection factor sequence and the time frequency analysis picture of a seismic sequence model of a seismic sequence model base; processing Hilbert-Huang transform for the seismic data of a target layer, finding a seismic sequence model of the target layer to judge the reflection character of the target layer. The invention utilizes Hilbert-Huang to analyze seismic section to attain accurate and reliable frequency spectrum and after decomposition display the effective information not displayed of the sections in the prior art, to provide data for reservoir predication.

The invention discloses a nuclear power plant loose part positioning method based on Hilbert transform and data screening. The method comprises the steps of dividing grids on an impacted object; mounting acceleration sensors; calculating a distance different between a distance from the center point of each grid to one acceleration sensor and a distance from the center point of each grid to another acceleration sensor; acquiring impact signals received by all the acceleration sensors; eliminating low-frequency noise of the signals by adopting a moving average method, and filtering environmental background noise by a Butterworth filter; performing Hilbert transform on the impact signals subjected to the noise elimination to obtain oscillation starting point moments of the impact signals; calculating a nominal transmission speed of the center point of each grid according to a transmission distance difference from the center point of the corresponding grid to the sensors and arrival time differences of all channels; and performing data screening on the nominal transmission speeds of the center points of all the grids, and calculating a variance; and finding out a minimum value in all the variances, recording coordinates of the center point, and displaying a positioning result. The nuclear power plant loose part positioning method has the advantages of high positioning precision and high searching speed.

The invention discloses a method and apparatus for measuring interhamonics of a power grid. The method comprises the following steps of: tracking the power grid by a PLL (Phase Locked Loop), accurately measuring frequency by a zero crossing detector matched with a DSP (Digital Signal Processor): sampling a power grid voltage (current) waveform at a constant sampling frequency, tracking the power grid frequency by the PLL, wherein the output of the PLL is used as a sampling clock of an ADC (Analog to Digital Converter); and then enveloping an extraction signal by adopting an HHT (Hilbert Huang Transform) method, carrying out FFT (Fast Fourier Transform) analysis on the enveloped signal, and finally figuring out information of harmonics and interhamonics of the power grid. The apparatus comprises three voltage transformers, three current transformers, an approximate synchronous sampling unit and an HHT-FFT computing unit. According to the invention, the HHT method is combined with the FFT method, power frequency carriers and envelopes are separated by using HHT and are respectively subjected to frequency spectrum analysis by using the FFT; and when the method and the apparatus are used in an actual power grid, measured harmonics and interhamonics precision and instantaneity meet the requirements of IEC (International Electrotechnical Commission).

The invention discloses a Hilbert-Huang Transform (HHT)-based high-frequency combined coding steady state visual evoked potential brain-computer interface method, which comprises the following steps of: connecting hardware, expressing different targets by using nn stimulation sequences formed by time sequence permutation and combination through n high-frequency stimulation frequencies, coding the stimulation sequences to form a code base, then executing electroencephalogram data characteristics by adopting a Hilbert-Huang Transform (HHT)-based variable frequency electroencephalogram signal characteristic extraction method, acquiring a Hilbert time-frequency graph of the electroencephalogram data, and comparing the extracted electroencephalogram data characteristics with the code base by using a local frequency spectrum extreme value target identification method to realize quantified target identification accuracy. The method has the advantages of simple operation, little electrode number, more target number, reduction of testee fatigue, and reduction of probability of inducing testee epilepsy.

The invention provides a power distribution network fault detection method based on a convolutional neural network, which provides the technical support for the three problems of a fault type, a faultphase and a fault position, and comprises the following steps of S1, collecting a power distribution network fault data set; S2, performing data preprocessing on the current sequence data by utilizing the simplified Hilbert-Huang transform; S3, initializing a convolutional neural network designed by the patent; S4, training the convolutional neural network by using the preprocessed data; and S5,testing the fault detection method by using the test set. According to the method, by utilizing the convolutional neural network and the simplified Hilbert-Huang transform, the fault type, phase and position of the power distribution network can be accurately judged, and meanwhile, compared with a method for carrying out fault judgment by utilizing a recurrent neural network, the speed is greatlyincreased.

The invention relates to a method for removing noises of a wind profile radar based on Hilbert-Huang transform. Based on a noise removing processor of the wind profile radar, the processor comprises an improved empirical mode decomposition module, a Hilbert transform module and a follow-up signal processing module. The method comprises the following steps that: 1) the noise removing processor of the wind profile radar receives an echo signal and utilizes the improved empirical mode decomposition module to obtain a series of natural mode function components of the echo signal; 2) the Hilbert transform is performed on each natural mode function component through the Hilbert transform module so as to obtain an energy spectrum value of the original signal; and 3) the follow-up signal processing module transforms the energy spectrum value of the original signal to obtain wind profile radar data after removing the noises. The invention has the following beneficial effects: the method has good adaptability, can obviously remove non-stationary interference, such as noises, and can especially and effectively remove the aliasing influence and the boundary effect of the adjacent frequencies.

The invention belongs to the electric power system electric energy quality monitoring and analysis field and relates to an empirical mode decomposition-based voltage anomaly characteristic identification method. The method includes the following steps that: a signal processor sets a sampling period as T for the voltage signals of an electric power system, and performs real-time sampling and quantization on the signals to obtain original signal v (t); empirical mode decomposition is performed on the original signals v (t), so that IMF modal components of various orders can be obtained; Hilbert-Huang transform is performed on the first IMF modal component of the original signal v (t), so that an instantaneous frequency graph and an instantaneous amplitude graph can be obtained; and voltage anomaly signals are detected and identified according to mutational point, amplitude change and instantaneous frequency trend characteristic information in the Hilbert-Huang transform graphs. With the empirical mode decomposition-based voltage anomaly characteristic identification method of the invention adopted, nonlinear and non-stationary signals of the electric power system can be processed, different characteristics of signals can be clearly identified in the Hilbert-Huang transform graphs, and electric energy quality detection analysis problems can be automatically extracted and correctly classified from massive voltage disturbance signals.

An embodiment of the present invention provides a Hilbert transform based high-precision digital holography phase reconstruction method, relating to the field of digital holography microscopy. By using the method, the precision of phase reconstruction can be increased. The method includes: recording a hologram; performing Fourier transform on the digital hologram to obtain a hologram frequency spectrum; filtering the hologram frequency spectrum to remove a zero-order spectrum; performing inverse Fourier transform on the filtered frequency spectrogram to obtain a hologram without the zero order item; performing two-dimensional Hilbert transform on the hologram; constructing an analytic function with the hologram without the zero order item and the Hilbert transform thereof; carrying out argument calculation for the analytic function to obtain a wrapping phase of the phase difference between an object light field and a reference light field; unwrapping the wrapping phase to obtain an unwrapped phase of the phase difference between the object light field and the reference light field; and extracting phase data from a region where the phase is a constant in the object light field, and performing linear fitting such that linear phase distortion correction is achieved and phase distribution of the object light field is obtained.

The invention discloses a blood sample level dependence functional magnetic resonance signal fluctuating frequency clustering analysis method which is characterized by comprising the following steps: step 1, pretreatment analysis is performed on resting state data collected from a magnetic resonance machine; step 2: Hilbert-Huang transform is adopted for decomposing the pretreated signals, so as to obtain intrinsic mode functions with different frequencies; the Hilbert-Huang transform comprises two steps of empirical mode decomposition and Hilbert transform; step 3, quantitative indexes of different frequency components are calculated; step 4, clustering of different voxels in a brain is performed by taking the quantitative indexes in the step 3, as a basis for classification and statistical analysis to the different indexes is performed. The method solves the problem of insufficiency of data drive frequency analysis methods in the fMRI research field, an iconography analytical method is provided for basic science and clinical researches and meanwhile new quantitative indexes and physiological markers are provided for clinical disease diagnosis and pathological mechanisms.

The invention discloses a method for measuring single phase ground fault distance of distribution network based on Hilbert-huang transform and genetic algorithm, which comprises the following steps of: based on accurate selection of fault line, using Hilbert-huang transform (HHT) to extract energy distribution characteristics of fault zero sequence current in different frequency bands at different moments; using genetic algorithm, taking fault distance, ground resistor and fault moment as variables, and taking energy distribution matching degree in different frequency bands as fitness function for feature matching of the original fault signals and the fault zero sequence current characteristics under various different fault conditions obtained by Matlab/Simulink simulation; and using the genetic algorithm to automatically search the fault conditions, taking the fault point with the best matched energy distribution as output, and obtaining distance, namely, output fault distance, from the fault point under the fault condition with the best matched fault characteristic to the bus. In this way, a novel method for extracting fault characteristics by using HHT, carrying out fault characteristic matching by using genetic algorithm and realizing measurement of single phase ground fault distance of distribution network is formed, and such method greatly improves accuracy in measurement of single phase ground fault distance of distribution network.

The invention relates to the field of electrical engineering and particularly relates to a CEEMDAN algorithm-based power system low-frequency oscillation mode identification method. According to the method, the actual measurement data, including rotor angle signals of a generator, of a power system are acquired by utilizing a wide-area measurement system. After that, the complete-set empirical mode of the adaptive noise of each group of original low-frequency oscillation signals is decomposed into Complete Ensemble Empirical Mode Decomposition with Adaptive Noise. The CEEMDAN algorithm is decomposed into a sum of a plurality of intrinsic mode functions (Intrinsic Modes) and IMFs, and each IMF component represents an oscillation mode. The energy value and the energy weight of each IMF component are calculated. Finally, the Hilbert-Huang transform is used for identifying the oscillation frequency and the damping ratio of the dominant oscillation mode, and the calculation result is compared with a characteristic value method. As a result, the safe and stable operation of the power system is ensured.

The invention relates to a digital down converter-Hilbert-Huang transform based instantaneous frequency measuring method, applied to the technical filed of radio. The method includes the following steps: 1) an original signal is subject to digital down conversion, and interested signal is converted into zero intermediate frequency; 2) the converted signal is subject to Huang transform, thus obtaining all the single frequency components; 3) each single frequency component signal is subject to Hilbert transform, and instantaneous frequency of the converted signal is solved; 4) the instantaneous frequency of the original signal is recovered according to the instantaneous frequency of the converted signal finally. The invention not only can show the advantage that the traditional Hilbert-Huang transform (HHT) can realize linearization and stabilization of non-linear and non-stationary signal, but also well overcomes frequency aliasing of the traditional HHT transform and shows higher frequency resolution. The invention is verified in a designed hardware system, and the algorithm can carry out effectively real-time estimation on frequency.

The invention relates to a method for mutual correlation phase difference measurement based on the Hilbert transform and the field of signal processing, in particular to a method for phase difference measurement. The method is suitable for a plurality of signal segments with the same center frequency. The method comprises the following steps that firstly, three signal segments are randomly selected from the signal segments with the same center frequency, wherein the three signal segments comprise two signal segments containing the phase difference to be calculated and one reference signal segment; secondly, the Hilbert transform is conducted on the reference signal segment or 90-degree phase shift is conducted on the reference signal segment so that a second reference signal segment can be obtained; thirdly, mutual correlation is conducted on the two reference signal segments and the two signal segments with the phase difference to be calculated so that four kinds of mutual correlation relationships can be obtained; finally, a phase difference value of tested signals can obtained according to the four kinds of mutual correlation relationships. According to the method for phase difference measurement, the phase difference of the signals can be accurately measured under the condition that the center frequency of the signals is unknown, and the accuracy and the robustness are good.