91 results about "Hilbert spectrum" patented technology

A computer implemented method of processing two-dimensional physical signals includes five basic components and the associated presentation techniques of the results. The first component decomposes the two-dimensional signal into one-dimensional profiles. The second component is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF's) from each profile based on local extrema and/or curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the profiles. In the third component, the IMF's of each profile are then subjected to a Hilbert Transform. The fourth component collates the Hilbert transformed IMF's of the profiles to form a two-dimensional Hilbert Spectrum. A fifth component manipulates the IMF's by, for example, filtering the two-dimensional signal by reconstructing the two-dimensional signal from selected IMF(s).

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

The invention provides a cavitation noise modulation feature extraction method based on an empirical mode. The method comprises the following steps: firstly standardizing a short cavitation noise signal; carrying out bandpass filtering on the standardized signal to obtain the bandpass signal of cavitation noise; carrying out envelope detection on the bandpass signal to obtain an envelope signal; carrying out lowpass filtering on the envelope signal to obtain a low-frequency envelope signal; decomposing the low-frequency envelope signal into a plurality of intrinsic mode functions (IMFs) by using empirical mode decomposition analysis; selecting the optimum IMF through evaluation; carrying out Hilbert transformation on the optimum IMF to obtain a Hilbert spectrum of the optimum IMF; and calculating the instantaneous frequency at every moment by using the Hilbert spectrum, so as to complete cavitation noise modulation feature extraction. According to the method provided by the invention,based on the adaptability of empirical mode decomposition and high resolution of Hilbert-Huang transformation, the disadvantage of the traditional modulation feature extraction method that modulationfeature extraction is difficultly carried out on short-time and non-stably modulated cavitation noise data can be overcome.

The invention discloses a current transformer (CT) saturation detection method based on Hilbert-Huang transformation (HHT), and the method comprises the followings steps that instantaneous frequency spectrum, Hilbert spectrum and Hilbert marginal spectrum are obtained through EMD decomposition and Hilbert diversion by collecting differential current signals on both sides of a transformer; the detection of saturation faults and CT saturation outside a transformer area is completed respectively arranged to the three types of spectrum so as to realize the quick action and the reliable action of differential protection under CT saturation; the method serves as a preferential improvement to a traditional CT saturation detection method and combines instantaneous frequency criteria, Hilbert spectrum criteria and Hilbert marginal spectrum criteria in the forms of 'and' and 'or'; when a judgment result has errors, another method can correctly judge, so that the reliability of differential protection is improved; and in addition, fault current outside the transformer area, fault current in the area, conversion fault current and the like under the two circumstances of CT saturation and unsaturation are fully considered, and the CT saturation detection method based on HHT has the advantages of stronger functions, higher efficiency, higher reliability and the like.

The invention discloses a circuit breaker fault diagnosis method based on a Hilbert-Huang transform algorithm. Empirical mode decomposition (EMD) is carried out on data signals, through the EMD on the signals, each intrinsic mode function (IMF) component is obtained, Hilbert transform is carried out on each IMF component, instantaneous amplitude and instantaneous frequency parameters are obtained, the amplitude and the instantaneous frequency are drawn to a Hilbert spectrum of energy, frequency and time distribution, the Hilbert spectrum is applied to analysis of nonlinear and nonstationary series, and a mirror extension method is adopted to suppress Hilbert transform endpoint effects. The testing result proves that the method has flexibility and effectiveness in vibration monitoring and analysis on the power system circuit breaker, the EMD is used for decomposing the signals to n IMF components, different frequency components in the vibration signal are in the decomposed IMF component, the frequency distribution in the IMF2 just contains a basic frequency part of a disconnecting switch, the analysis precision is improved, and the analysis steps are simplified.

The invention provides a transient signal detection method based on HHT double noise reduction. The method comprises the steps of initializing basic parameters including a width threshold, an empirical mode decomposition (EMD) termination order, a wavelet detection threshold multiple, an energy density threshold multiple, a frequency resolution coefficient and a first-order recursive coefficient; determining an effective intrinsic mode function (IMF) through EMD wavelet detection, rejecting IMF components merely containing noise to achieve primary noise reduction; solving the square of a Hilbert spectrum through effective IMF components, and performing local integration along a frequency axis to obtain a local transient energy density level so as to achieve secondary noise reduction; and calculating local transient energy density envelopes of signals, taking the local transient energy density envelopes as detection statistics, performing binary decision for signals, and constructing a local transient energy density detector. The transient signal detection method is really self-adaptive, provided with strong noise reduction capacity and applicable to environments of low signal-to-noise ratios.

The invention discloses a Hilbert-Huang Transform end effect inhibition method based on a grey theory, and relates to a characteristic extracting method in the field of signal processing. The method aims at solving the problems that a current Hilbert-Huang Transform method is interfered by the end effect and can not effectively extract the substantive characteristics of a signal and can not obtain accurate intrinsic modal functions and Hilbert spectrums. The method is as follows: on the one hand, the grey theory is adopted to predict an extreme point worked out by traditional EMD (Empirical Mode Decomposition) to the left and right; and an original extreme point and the predicted extreme point are used for solving the envelope and calculating the accurate intrinsic component IMF of an original signal; on the other hand, the grey theory is used to extend the data at the two ends of each IMF worked out by the EMD (empirical mode decomposition), and then implement Hilbert transform to obtain the Hilbert spectrums. The Hilbert-Huang Transform end effect inhibition method based on the grey theory has the advantages of less input data volume required by the grey model, high short period prediction precision, rapid calculation speed, and effective processing on nonlinear or nonstationary signals. The Hilbert-Huang Transform end effect inhibition method is applied to the field of signal processing.

The invention discloses a vibration signal analysis method for a main bearing of a wind power transmission system based on improved HHT and fuzzy entropy. The method comprises the steps of collectinga vibration signal, performing EMD decomposition on the vibration signal, screening a real IMF component by a relative entropy theory, and performing Hilbert spectrum analysis to obtain marginal spectrum through synthesis. According to the method, the fault characteristics of the main bearing of the wind power transmission system can be stably and effectively extracted, and the interference of thenoise to a characteristic frequency can be effectively filtered. A false eigenmode function component generated by empirical mode decomposition is eliminated with proposed improved Hilbert-Huang transform, the marginal spectrum synthesized by the real component is made, and the true frequency distribution of the signal is reflected. Combined with the fuzzy entropy theory, an eigenmode function which best reflects fault bearing characteristics is screened out.

The invention discloses a fault diagnosis device and a method for an antifriction bearing based on the analysis on the morphological component of an acoustic signal. During the rotation process, the bearing generates an acoustic signal. Through analyzing the time domain features of the acoustic signal, a novel bearing fault diagnosis method based on the MCA and Hilbert spectrum analysis of the acoustic signal is provided. The result of the simulation and example analysis shows that, the fault acoustic signal of the bearing is formed through compounding an impulse component, a resonance component and a noise component. Through constructing a redundant dictionary composed of a coif 4 wavelet dictionary and a local cosine dictionary, the good approximation to the original signal is realized. Based on the MCA method, the generalized soft thresholding operation and the de-noising are realized. Meanwhile, the impulse component of the original signal is sparsely isolated. Finally, in combination with the impulse component of the original signal and the Hilbert frequency spectrum thereof, isolated based on the MCA method, the fault type of the antifriction bearing can be accurately identified.

The invention provides a hydraulic-pump fault feature signal extraction method based on local main value decomposition. Endpoint continuation processing is conducted on collected hydraulic-pump fault signals with a related image method, so that signal endpoints are extreme points; smooth estimation is conducted on extreme values and local mean values with the method of fitting an envelope line through a cubic B spline, so that fitting errors are reduced; then, LMD decomposition is conducted on the preprocessed signals to obtain PF components, correlation analysis is conducted on PFs and the original signals, the real PF components are selected, and finally Hilbert transformation is conducted on the real PF components to obtain Hilbert spectrums of the signals, wherein the Hilbert spectrums are signal features of the hydraulic-pump fault signals. The method is suitable for fault diagnosis of a hydraulic pump in the industrial field.

The invention relates to a three-dimensional grid processing method based on empirical mode decomposition and Hilbert spectrum calculation of a space filling curve. The method comprises six steps of an initial stage, namely, a space filling curve generation stage, of generating the space filling curve by an input three-dimensional grid model, wherein a Hamiltonian circuit is used as the space filling curve; an input signal defining stage of adopting average curvature of each point as a signal value of each point of a three-dimensional grid, wherein a signal can be converted into a one-dimensional signal according to a global sequence number of data points in the Hamiltonian circuit; an empirical mode decomposition stage of decomposing the one-dimensional signal into a plurality of intrinsic mode functions and one residual; a Hilbert spectrum calculation stage of performing Hilbert spectrum calculation on the intrinsic mode functions in the previous stage to generate an instantaneous frequency and instantaneous amplitude; a filter design stage; and a model reconstruction stage of processing the signal to obtain a new signal and reconstructing the three-dimensional grid. Therefore, the processing and analysis of the three-dimensional grid are realized.

The invention discloses a ship classification method based on vibration noise identification, and the method comprises the following steps: 1, collecting ship vibration noise, and converting the shipvibration noise into a time domain signal; 2, performing improved ensemble empirical mode decomposition on the ship vibration signals; 3, performing Hilbert-Huang transform on the signal after modal decomposition; 4, performing Hilbert spectrum analysis on ship radiation noise; 5, extracting ship noise characteristics; 6, classifying the ship noise characteristics by using a ship classifier basedon a support vector machine; 7, verifying the accuracy of the ship classification result, and if the result is correct, ending ship classification; and if not, entering the ship classifier for re-classification. The ship classification method based on vibration noise identification is realized, and a new way for marine traffic management personnel, scientific research personnel and military personnel to acquire ship types is provided.

The invention provides a fault diagnosis method for a fire pump operating at a low speed. The fault diagnosis method aims to achieve fault diagnosis of the fire pump operating at a low speed at the non-fire state. The fault diagnosis method includes the specific steps that a high-frequency stress wave sensor carries out signal detection on the fire pump at the inspection state; a high-frequency stress wave composite signal, containing noise, of the fire pump operating at a low speed is collected; the signal is processed through a FastICA algorithm, and a source signal is estimated; the Hilbert-Huang Transform is carried out on the estimated signal, and an n-order intrinsic mode function and a Hilbert spectrum of a fault signal are obtained to serve as a feature vector for judging the state of the fire pump and recognizing faults; finally, a support vector machine is utilized for classifying the faults. The fault diagnosis method is suitable for processing non-linear and non-stable signals under the situation of a low speed, more complete fault features can be obtained, and the fault diagnosis effect is better.

The invention discloses a novel multi-feature fusion electrocardio authentication method. According to the novel multi-feature fusion electrocardio authentication method, multi-feature fusion of a Hilbert spectrum and a decision layer is firstly applied to the authentication method of the electrocardio, the Hilbert spectrum including integrated features of the time domain, the frequency domain and the energy of an electrocardio signal is selected and obtained through EEMD, Hilbert spectral transformation and Hilbert spectrum analysis, a two-dimensional CNN authentication system and a one-dimensional CNN authentication system are used for calculating corresponding matching scores for the electrocardio signal authentication, then the decision layer is merged to make the final authentication decision, and a complete electrocardio certification system with higher robustness and generalization capability is obtained.

The invention provides a needling machine transmission mechanism vibration signal processing method based on combination of MEEMD and an exponential wavelet threshold. The needling machine transmission mechanism vibration signal feature extraction method comprises the steps of: carrying out endpoint continuation processing on an acquired needling machine transmission mechanism vibration signal byadopting a waveform matched self-adaptive endpoint continuation method; performing wavelet decomposition, and denoising and reconstructing the acquired signal by utilizing an exponential wavelet threshold function to obtain the denoised signal; performing envelope line fitting on the denoised vibration signal by adopting a cubic B-spline method, and then carrying out MEEMD decomposition on the signal to obtain cross correlation coefficients and kurtosis values of a plurality of IMF components and the denoised signal; performing correlation analysis on the IMFs and the original signal to selectthe useful IMF; and finally performing Hilbert transform on the IMF components to obtain a Hilbert spectrum of the signal, and regarding the Hilbert spectrum as a signal feature of the needling machine transmission mechanism vibration signal. The needling machine transmission mechanism vibration signal feature extraction method is suitable for fault diagnosis of a needling machine transmission mechanism in the industrial field.

The invention discloses a transformer winding state diagnosis method. The transformer winding state diagnosis method comprises a first step of collecting vibration signals of a transformer box wall when a transformer is short-circuited; a second step of performing prolongation pretreatment on left end points and right end points of the vibration signals; a third step of decomposing the vibration signals undergone prolongation into a plurality of product function components; a fourth step of enabling all the product function components obtained by decomposition to undergo Hilbert transform, and obtaining a Hilbert spectrum of the vibration signals; a fifth step of obtaining a Hilbert marginal spectrum and Hilbert energy according to the Hilbert spectrum; and a sixth step of distinguishing a state of a transformer winding according to the Hilbert marginal spectrum and variations of the Hilbert energy.

The present invention provides a method of implementing the high dimensional Holo-Hilbert spectral analysis which transforms a data from time domain to frequency domain. At the first of the steps, obtaining an amplitude intrinsic mode component and an instantaneous frequency component of the data by a mode decomposition, such as using Empirical Mode Decomposition (EMD), adaptive filtering, or optimal basis pursue, etc to show a plurality of amplitude intrinsic mode functions (amplitude IMFs) and a plurality of frequency intrinsic mode functions (frequency IMFs). Then, analyzing each of the amplitude IMFs and the frequency IMFs to obtain a plurality value in different high order components. At the last, to establish a high dimensional Holo-Hilbert spectrum by combining the high order component with the original component to show the interaction between frequency and amplitude. Consequently, the present invention not only discloses a spectrum that can represent all the possible processes: additive and multiplicative, intra- and inter-mode, stationary and nonstationary, linear and nonlinear interactions, but also makes a new index for quantifying the inter-mode degree of nonlinearity possible.

The invention discloses a ferromagnetic resonance fault detection method for an isolated neutral system. Three-phase voltage and a zero-sequence voltage signal of a bus are analyzed to detect the ferromagnetic resonance fault of the system. According to the method, with Hilbert Huang transform (HHT) based on empirical mode decomposition (EMD) as the core, in combination with FFT, FFT analysis is firstly performed on the three-phase voltage and the zero-sequence voltage signal, then HHT analysis is performed, and finally the moment of ferromagnetic resonance and the amplitude value of ferromagnetic resonance over-voltage are obtained through a three-dimensional Hilbert spectrum, so that the ferromagnetic resonance type, the amplitude value and the inducing moment are accurately analyzed in a quantified mode. The HHT signal analysis method is adopted for completely getting rid of linearity and stability constraints, namely, frequency-domain analysis can be performed on the voltage signal, and meanwhile time-domain analysis can be performed; in the process of HHT analysis, an RBF-point symmetry continuation method is adopted, in combination with a mirror image continuation method, the end effect of HHT is improved, and mode superposition is avoided.

The invention provides a radiation source recognition method and a radiation source recognition device. The radiation source recognition method comprises the following steps: A, acquiring a first number of radiation source training signals, and using as a training sequence; B, gaining the Hilbert spectrum of the first number of radiation source training signals; C, gaining the correlation coefficients between the Hilbert spectrum of each training signal and the Hilbert spectrum of other training signals, and forming training vectors; D, training a classifier according to the training vectors and category marks corresponding to the training signals so as to distinguish different radiation sources; E, obtaining a second number of radiation source test signals, and using as a test sequence; F, gaining the Hilbert spectra of the second number of radiation source test signals; G, gaining the correlation coefficients between the Hilbert spectrum of each test signal and the Hilbert spectrum of other test signals, and forming test vectors; H, classifying the test vectors in the step G by the classifier, and distinguishing the radiation source of the test sequence. Dot-to-dot scenes can be expanded to relay scenes, so that each category of radiation sources is recognized.

The invention relates to a grounding line selection method based on HHT signal analysis, which comprises the steps of 1) performing single-phase grounding fault analysis and fault line selection on aneural point non-effective grounding system by adopting HHT; 2) introducing an EEMD (Ensemble Empirical Mode Decomposition) method into signal sudden change detection, detecting the moment when a zero-sequence current signal of a signal feeder reaches a bus, and starting fault line selection; 3) extracting zero-sequence current transient signals, designing a low-pass filter with the window being apower frequency so as to filter out steady state signals in the zero-sequence current of each line and extract transient signals; 4) performing EEMD analysis on transient current i0k(t), that is, performing EEMD analysis on the zero-sequence transient current i0k(t), decomposing the zero-sequence current transient component i0k(t) containing various frequencies into a series of current IMFn components with different central frequencies, and searching a main frequency component concentration IMF; 5) performing time-frequency spectrum (Hilbert spectrum) analysis on the zero-sequence current signal of the feeder, that is, performing normalized Hilbert transform on the maximum IMFi obtained by EEMD analysis to obtain a time-frequency spectrum (Hilbert spectrum) of the zero-sequence current signal of the feeder.

The invention belongs to the positioning or presence detection technology field utilizing radio wave reflection or re-radiation and discloses a radar signal modulation mode analysis method based on the improved HHT and a signal processing system. The radar signal time-frequency analysis method integrated with wavelet packet decomposition, CEEMD and correlation spectrum filtering is utilized to obtain each eigenmode component, improved Hilbert transform is utilized to obtain Hilbert spectrum instantaneous frequency distribution of a radar signal, and time-frequency parameter extraction of the radar signal is carried out. The method is advantaged in that a frequency component of an original signal is accurately extracted through utilizing the wavelet packet decomposition method for de-noising in combination with envelope correlation spectrum screening, problems of false components and modal aliasing caused by the noise are suppressed, a problem of the inaccurate HHT analysis result due to modal aliasing and the false components is effectively overcome, time-frequency distribution that is more aggregated and adapts to a lower signal-to-noise ratio is obtained through improved Hilberttransform, and high accuracy is realized.

The invention discloses a frequency hopping signal detection method based on fractional order wavelet transform and Hilbert-Huang transform, which is characterized by comprising the following steps: 1) inputting a frequency hopping signal to be processed; 2) setting an order range and a search step length, and performing fractional Fourier transform on the frequency hopping signal; 3) obtaining anapproximation coefficient of the highest layer and a wavelet detail coefficient of each layer; 4) carrying out wavelet reconstruction; 5) obtaining a de-noised time domain signal; 6) obtaining new time domain signal; 7) carrying out EMD decomposition to obtain Hilbert spectrum; 8) obtaining frequency hopping period. According to the method, cross item interference does not exist, the influence ofa window function in a traditional time-frequency analysis process is avoided, multi-resolution analysis of the signal in a time-fractional domain is achieved, and the signal parameter estimation precision is further improved.