23 results about "Hilbert spectrum" patented technology

The invention discloses an excitation load grounding fault detection method, system and device based on EEMD and Hilbert spectrum analysis and a medium, and belongs to the technical field of power system energy storage equipment, and the method comprises the steps: collecting and preprocessing an electric signal, and constructing a discretization time domain data sequence; performing iterative decomposition on the sequence to obtain multiple groups of components; time domain structure analysis is carried out, and target components containing fault mutation features are screened; frequency domain transformation is carried out on the target component to construct spectrum distribution, and normalization processing is carried out; and then time-frequency joint transformation is carried out, energy distribution and energy entropy indexes are calculated, and diagnosis is completed. According to the method, modal aliasing is suppressed by using EEMD adaptive noise injection and a set average strategy, accurate separation of high-frequency transient disturbance and low-frequency harmonic waves is realized, multi-dimensional feature combined diagnosis is formed by combining frequency domain screening and time-frequency analysis, and the fault detection precision and reliability are improved.

The invention relates to the technical field of fan blade fault diagnosis, and discloses a fan blade fault diagnosis system and method based on HHT and DBSCAN. Synchronously acquiring vibration signals acquired by a three-axis acceleration sensor and wind speed and rotating speed working condition data acquired by an SCADA (Supervisory Control and Data Acquisition) system; eMD empirical mode decomposition is carried out on the collected vibration signals, the vibration signals are decomposed into a limited number of IMF signals, and effective IMF signals are screened; generating a Hilbert spectrum through HHT, extracting multi-scale frequency band energy features, and fusing SCADA data to carry out working condition adaptive normalization on the features; and then, processing the normalized features by adopting a self-adaptive DBSCAN clustering algorithm based on K-distance map parameters, and realizing fault diagnosis by identifying an abnormal cluster with a sample proportion of less than 5% and combining with a frequency band energy deviation threshold. According to the method, the problems that a traditional method is not thorough in decomposition of non-stationary signals, depends on labeled samples and is insufficient in clustering robustness are solved, and unsupervised and high-precision blade state monitoring is achieved.

The invention relates to the technical field of inertial sensor state data analysis, and discloses a frequency domain processing-based inertial sensor data analysis method, which realizes frequency domain analysis on actual error composition of an inertial sensor, thereby improving the accuracy of effective system performance analysis of the inertial sensor. The method specifically comprises the following steps: S1, acquiring an original output signal by adopting an MEMS inertial measurement unit of a sensor; s2, decomposing a time domain signal in the original output signal into a solid mode equation and a residual term through a mode decomposition algorithm; extracting a solid-state model equation by using a screening algorithm through empirical mode decomposition, and updating a residual error; selecting a center frequency by using signal characteristics and priori knowledge through variational mode decomposition, extracting a solid-state mode equation at the center frequency, and modeling as a function about amplitude phase frequency; s3, performing Hilbert-Huang transform on the components of the solid mode equation to generate a Hilbert spectrum in time-frequency domain joint distribution; and S4, constructing a sensor performance evaluation model based on the Hilbert spectrum.

The invention relates to the technical field of ultrasonic road surface recognition, in particular to a road surface pothole area recognition method for road repairing and flattening. The method comprises the following steps: acquiring an ultrasonic echo signal sequence; after the ultrasonic echo sequence is equally divided, the local scattering degree is determined based on the equally divided waveform entropy difference; the waveform entropy of the ultrasonic echo signal sequence forms an entropy sequence, after coarse graining is conducted on the entropy sequence, the optimal time delay and the optimal embedding dimension are obtained, and then a state vector is determined; constructing a recursive matrix based on the similarity of the state vectors, and determining a first surface feature based on the feature of the recursive matrix and the local scattering degree; a second surface feature based on an entropy value of frequency energy in a Hilbert marginal spectrum converted based on an ultrasonic echo signal sequence and a Hilbert spectrum time point amplitude; complexity is determined based on the surface features, the complexity serves as a weight to adjust an amplitude threshold value, and then the signal is enhanced; and identifying the pothole area by comparing the enhanced signal with the safety signal. According to the invention, the detection precision of the pothole area is improved.

The application discloses a kind of PCCP pipeline abnormal vibration high frequency automatic identification method, system and equipment, belong to concrete pipeline vibration automatic monitoring field. Including: the modal decomposition is carried out to the broken wire vibration data monitored, and Hilbert spectrum calculation is carried out, obtain time-frequency spectrum;Calculate the vibration energy curve after each filtering with time variation;Curve is normalized, and the one-dimensional vibration energy data after normalization is dimension, form filter threshold-time-filtered energy matrix;Based on energy-time curve before filtering selects key analysis time period, and obtains the corresponding filter threshold-time-filtered energy matrix sub-matrix;To the row (or column) of each filter frequency threshold value corresponding to sub-matrix selects upper quartile and handles, form filter frequency threshold-normalized filtered energy mean curve;According to the maximum on the curve, obtain the highest frequency of PCCP pipeline abnormal vibration.The application can realize automatic identification.

The application discloses a GIS interference source positioning method based on VFTO analysis, electronic equipment and a storage medium, and is used for a WAPI device of a GIS system. The method comprises the following steps: extracting VFTO waveform data in a wireless local area network security protocol AP of the GIS system; performing noise reduction processing on the VFTO waveform data, and decomposing the VFTO waveform data after noise reduction into a plurality of intrinsic mode functions of different frequency components; performing Hilbert transformation on each intrinsic mode function, and constructing a Hilbert spectrum; and positioning an interference source based on the propagation characteristics of the interference signal and the physical layout of the system through the Hilbert spectrum. The VFTO generated by the WAPI device is analyzed and processed, and the interference source is determined, so that the complex industrial environment of strong electromagnetic interference can be coped with, and the stability and reliability of the WAPI wireless environment are ensured.

The application discloses an oscillation wave detection-based extra-high voltage power transformer winding fault simulation device and diagnosis method. Firstly, the test transformer winding is tested, the transformer winding connection point is connected with a high-frequency high-voltage switch and a high-frequency high-voltage direct current power supply, the high-frequency high-voltage switch is periodically actuated, thereby exciting the extra-high voltage transformer body structure vibration; according to the test data, an ITD transformation is adopted to establish a signal model X t ; then, an empirical mode decomposition (EMD) method is adopted to decompose the signal into a sum of a plurality of mutually orthogonal intrinsic mode function (IMF) components, a Hilbert transformation is performed on each IMF component to obtain an instantaneous frequency and an instantaneous amplitude, thereby obtaining a Hilbert spectrum of the signal to perform decomposition and reconstruction, improve the signal-to-noise ratio, calculate I(X,Y) to judge and remove most of the redundant noise and interference signals; further reconstruct the required signal, after the EMD decomposition of the non-steady-state signal, the mutual information is utilized to screen the obtained IMF components of each order, and a new x(t) is obtained. Finally, the winding vibration signal after the simulation deformation fault is solved through a signal sequence sample entropy calculation formula, and the winding deformation fault severity is judged, and the transformer fault degree is identified.

The application provides a flexible direct current power transmission system fault detection method, comprising the following steps: obtaining an original current signal; setting a search range for a parameter of a variational mode decomposition, thereby obtaining an original setting parameter; decomposing the original current signal into mode functions with different center frequencies; selecting a value corresponding to a minimum relative entropy as an optimal parameter; performing a final variational mode decomposition on the original current signal by using the optimal parameter, thereby obtaining final mode functions in an ordered arrangement; selecting a second final mode function from the final mode functions; performing a Hilbert-Huang transform on the second final mode function, thereby obtaining a Hilbert spectrum of the second final mode function; performing a synchronous compression transform on the Hilbert spectrum, thereby obtaining an instantaneous energy density of the Hilbert spectrum; and comparing an amplitude of the instantaneous energy density with a steady-state threshold, thereby realizing fault detection. The application can realize accurate and rapid fault detection on a flexible direct current power transmission system.

This application relates to the technical field of vehicle control, and in particular to a method, apparatus, device, and medium for testing time synchronization networks. The method includes: acquiring the state equation and system parameters of a preset nonlinear dynamic disturbance; injecting a phase perturbation sequence into the device under test (DUT) to capture the synchronization error response sequence output by the DUT under perturbation excitation; performing variational mode decomposition on the synchronization error response sequence to obtain K eigenmode functions with finite bandwidth; performing a Hilbert transform on each eigenmode function to obtain the Hilbert spectrum of the tested spatiotemporal synchronization loop; constructing a parameterized reduced-order model of the system based on the eigenmode functions, predicting the stability domain boundary and instability bifurcation point of the system in the parameter space, and generating a synchronization stability boundary map to verify the bidirectional synchronization capability of the DUT based on the synchronization stability boundary map. This method can improve testing efficiency by adding active excitation to deeply evaluate the dynamic performance of the device and thus performing automatic time testing.

The application discloses a power transmission line robot automatic icing identification and deicing method, the method comprises the following steps: obtaining ice layer dielectric properties and meteorological data, calculating ice energy level index; after successful connection, based on quantum heuristic algorithm, point cloud matching is performed on the ice layer and an ideal power transmission line spiral stranded model, and a topological singular point density value is calculated to dynamically generate an adaptive fractal rolling path; in the moving rolling process, the Hausdorff dimension change of ice cracks and the nonlinear distortion component of the roller motor are fused, and a brittle-plastic phase transition critical factor is calculated; for the ice crushing removal effect, the sound force coupling dissipation rate is calculated through the Hilbert spectrum of the acoustic emission signal, and a model predictive control framework is constructed to realize efficient centrifugal throwing of ice chips; after the operation section is finished, a global residual risk potential field is generated based on the sound force coupling dissipation rate, and secondary fine polishing or intelligent unhooking return is performed according to the cleanliness convergence value. The application improves the deicing efficiency while ensuring the safety of the power transmission line body.

The application discloses a kind of underwater vehicle propeller shaft frequency and blade frequency variational holographic spectrum identification method and device, variational holographic spectrum method utilizes the thought of holographic representation, realizes the robust high-dimensional holographic representation of nonlinear non-stationary signal, obtains variational holographic spectrum diagram, by feature extraction to variational holographic spectrum diagram, realize the identification and estimation of underwater vehicle propeller shaft frequency, blade frequency and blade number;The method introduces variational mode decomposition, effectively solves the mode aliasing problem of EMD in holographic hilbert spectrum analysis method;According to the characteristics that propeller modulation signal in underwater vehicle radiation noise is low frequency slow change, segmented fourier transform is introduced, the problem that there is large error when hilbert spectrum analysis method is processed low frequency slow change signal is solved.The method solves the problem of insufficient representation when the commonly used radiation noise analysis method LOFAR analysis, DEMON analysis and various improved algorithms are analyzed in nonlinear non-stationary signal.

The application relates to the technical field of vehicle-mounted control, in particular to a time synchronization network test method and device, equipment and a medium, the method comprising the following steps: acquiring a state equation and system parameters of a preset nonlinear dynamic disturbance; injecting a phase perturbation sequence into a measured device; capturing a synchronization error response sequence output by the measured device under the perturbation excitation; performing variational modal decomposition on the synchronization error response sequence to obtain K intrinsic modal functions with limited bandwidth; performing Hilbert transformation on each intrinsic modal function to obtain a Hilbert spectrum of the measured time-space synchronization loop; constructing a parameterized reduced-order model of the system based on the intrinsic modal functions, predicting a stable domain boundary and an unstable bifurcation point of the system in a parameter space, and generating a synchronization stability boundary atlas to verify bidirectional synchronization capability of the measured device according to the synchronization stability boundary atlas. The dynamic performance of the device can be deeply evaluated by increasing active excitation, so that automatic time testing is performed, and the testing efficiency is improved.

This application relates to the field of ultrasonic pavement recognition technology, specifically to a method for identifying pothole areas in road repair and leveling. The method includes: acquiring an ultrasonic echo signal sequence; dividing the ultrasonic echo sequence equally and determining the degree of local dispersion based on the waveform entropy difference of the division; constructing an entropy sequence from the waveform entropy of the ultrasonic echo signal sequence, coarsening it, obtaining the optimal time delay and optimal embedding dimension, and then determining the state vector; constructing a recursive matrix based on the similarity of the state vectors, and determining the first surface feature based on its features and the degree of local dispersion; determining the second surface feature based on the entropy value of the frequency energy in the Hilbert marginal spectrum transformed from the ultrasonic echo signal sequence and the amplitude at time points in the Hilbert spectrum; determining the complexity based on the surface features, using it as a weight to adjust the amplitude threshold, thereby enhancing the signal; and identifying pothole areas by comparing the enhanced signal with a safe signal. This application improves the detection accuracy of pothole areas.

This invention relates to the field of reservoir prediction technology, and particularly to a method and apparatus for establishing a low-frequency model based on Hilbert-Huang transform for seismic inversion. The method includes: partially stacking CRP gathers that reflect AVO characteristics from CRP gather data; decomposing the partially stacked data volume into several intrinsic mode function (IMF) data volumes; performing a Hilbert-Huang transform on the IMF data volumes to obtain a Hilbert spectrum; performing low-frequency energy enhancement on the partially stacked data volumes based on the Hilbert spectrum to obtain a low-frequency enhanced data volume; and establishing a low-frequency model using a support vector machine method combined with well-seismic analysis, under the constraints of a subdivided layer interpretation structural framework. This invention expands the effective signal at the low-frequency end without changing the relative relationships between the partially stacked data volumes, improving the vertical resolution of seismic data while maintaining its lateral resolution, thus providing effective technical support for unconventional reservoir oil and gas exploration and development.

The invention provides a PVDF (polyvinylidene fluoride)-based blasting monitoring system and a PVDF-based blasting monitoring method, which can realize accurate capture and time sequence analysis of each blasting event so as to accurately evaluate the blasting quality and provide a reliable basis for safe danger elimination, and comprises a vibration sensing module, a signal acquisition terminal and a control module, the signal acquisition terminal processes the electric signal, converts the electric signal into a digital vibration signal and transmits the digital vibration signal; the data monitoring terminal processes the received digital vibration signal by using an HHT algorithm to generate a Hilbert spectrum representing the time-frequency characteristic of the vibration signal; based on the Hilbert spectrum, identifying an effective blasting signal corresponding to the single blasting event; according to the time sequence of the effective blasting signals, an actual blasting time sequence is reconstructed; and the actual blasting time sequence is compared with a preset blasting scheme time sequence, and the blasting operation effect is judged.

The present application relates to the technical field of inertial sensor state data analysis, and discloses a data analysis method for an inertial sensor based on frequency domain processing, which realizes frequency domain analysis on the actual error composition of the inertial sensor, thereby improving the accuracy of effective coefficient performance analysis of the inertial sensor. Specifically, S1, a MEMS inertial measurement unit of the sensor is used to collect original output signals; S2, a time domain signal in the original output signal is decomposed into a solid mode equation and a residual term through a modal decomposition algorithm; the solid mode equation is extracted through empirical mode decomposition using a screening algorithm, and the residual is updated; the center frequency is selected using signal characteristics and prior knowledge through variational modal decomposition, the solid mode equation at the center frequency is extracted, and modeling is performed as a function of amplitude, phase and frequency; S3, Hilbert spectrum of time-frequency domain joint distribution is generated by performing Hilbert-Huang transformation on the solid mode equation component; and S4, a sensor performance evaluation model is constructed based on the Hilbert spectrum.

The invention provides a fault detection method for a flexible DC power transmission system. The method comprises the following steps: acquiring an original current signal; setting a search range for the parameters of the variational mode decomposition so as to obtain original set parameters; decomposing the original current signal into modal functions with different center frequencies; selecting a value corresponding to the minimum relative entropy as an optimal parameter; carrying out final variational mode decomposition on the original current signal by adopting the optimal parameter to obtain orderly arranged final mode functions, selecting a second final mode function, and carrying out Hilbert-Huang transform on the second final mode function to obtain a Hilbert spectrum of the second final mode function; and performing synchronous compression transformation processing on the Hilbert spectrum to obtain the instantaneous energy density of the Hilbert spectrum, and comparing the amplitude of the instantaneous energy density with a steady-state threshold to realize fault detection. According to the invention, accurate and rapid fault detection of the flexible DC power transmission system can be realized.

The application discloses a DEMON spectrum analysis frequency band selection method and device based on holographic Hilbert spectrum analysis, which utilizes short-time Fourier transform to perform time-frequency analysis on a signal, and combines a time-domain graph to intercept stable time-domain signals; the intercepted signals are subjected to holographic Hilbert spectrum analysis, a nested empirical mode decomposition and Hilbert-Huang transform method is used, an additional dimension is added in a frequency spectrum result, a high-dimensional representation method is used to establish a corresponding relationship between a modulation frequency and a carrier frequency; then, a holographic Hilbert amplitude modulation spectrum is combined to determine a frequency band range of a band-pass filter in DEMON spectrum analysis; finally, the signal is subjected to DEMON spectrum analysis to accurately estimate parameter information such as an axial frequency, a blade frequency and a number of blades of an underwater target propeller. The application directly obtains the corresponding relationship between the modulation frequency and the carrier frequency by using the holographic Hilbert spectrum decomposition technology, and solves the problem that it is difficult to select the band-pass filter in the DEMON spectrum analysis process.

The invention discloses a millimeter wave radar vital sign detection method and system based on ICEEMDAN combined with Hilbert spectrum analysis, and the method comprises the steps: building a millimeter wave radar experiment system, placing a radar in front of a human body, collecting a signal, obtaining an IF signal, and carrying out the preprocessing of the IF signal; static clutters are suppressed by adopting a moving target display method, and a human body target position is limited through incoherent accumulation and a range gate. The phase of the vital sign signal is recovered by using the linear characteristic of arc tangent demodulation, and after the phase information of the target unit is extracted, the phase wrapping jump is eliminated through a phase unwrapping algorithm; after phase difference and moving average filtering are used for optimizing the phase signals, alternative component signals are obtained through an ICEEMDAN algorithm; and finally, performing spectrum analysis on the selected components through a Hilbert spectrum analysis algorithm to obtain an estimated frequency of the vital sign signal. The algorithm verification is carried out on the actually measured signal, and the result shows that the modal aliasing problem caused by the existing EMD algorithm in the signal modal decomposition process is reduced, and the high detection precision is obtained.

The invention relates to the technical field of intelligent control of food baking processing, and discloses a starch gelatinization control method and system for baking processing. According to the method, multi-frequency impedance signals in the starch gelatinization process are collected through an electrical sensor array, an intrinsic mode function component is extracted through empirical wavelet transform, a Hilbert spectrum is calculated, and then a gelatinization state feature vector is obtained. A linear prediction model is constructed by adopting a dynamic mode decomposition algorithm, a future system state is predicted, a control signal is generated in combination with a model prediction control algorithm, and power output of a baking heating unit is adjusted in real time. The technology effectively solves the problem that monitoring of the starch gelatinization degree is lagged in a traditional baking process, and the quality and stability of baked products are improved.

The invention provides a monitoring method based on holographic phase Hilbert spectrum analysis. The monitoring method comprises the following steps: introducing a stimulation signal; acquiring an electroencephalogram signal based on the stimulation signal; the holographic phase Hilbert spectrum analysis comprises the following steps: performing first-layer decomposition on the electroencephalogram signal by adopting a mode decomposition method to obtain a first-layer mode component which comprises a plurality of first-layer mode functions; calculating an envelope line of the first-layer mode function, and performing second-layer decomposition on the envelope line by adopting a mode decomposition method to obtain a second-layer mode component which comprises a plurality of second-layer mode functions; and constructing a holographic phase Hilbert spectrum for analyzing the relationship between the stimulation signal and the electroencephalogram signal.

The invention provides an intelligent tremor monitoring method based on holographic phase Hilbert spectrum analysis. The method comprises the following steps: acquiring a signal to be analyzed; synchronously acquiring EMG and ACC; performing holographic phase Hilbert spectrum analysis: performing first-layer decomposition on the EMG by adopting a mode decomposition method to obtain a first-layer mode component which comprises a plurality of first-layer mode functions; calculating an envelope of the first-layer modal function to obtain an envelope line; performing second-layer decomposition on the envelope line by adopting a mode decomposition method to obtain a second-layer mode component which comprises a plurality of second-layer mode functions; decomposing the ACC by adopting a mode decomposition method to obtain an ACC mode component which comprises a plurality of ACC mode functions; and constructing a holographic phase Hilbert spectrum for tremor analysis.