118 results about "Hilbert huang transformation" patented technology

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 discloses a flutter online monitoring method for machining equipment. The method comprises the steps that a proper sampling window is selected; empirical mode decomposition is carried out on sampled vibration signals; decomposed eigen modalities are screened to obtain a characteristic eigen modality; Hilbert transformation is carried out on the characteristic eigen modality to obtain a time-frequency spectrum; statistical pattern analysis is carried out on the time-frequency spectrum to obtain characteristic parameters; the statistical characteristic parameters are compared with a set characteristic threshold value and the statistical characteristic parameter of a historical adjacent signal, and the vibration state of a system is judged. The flutter online monitoring method aims to solve the problems that a flutter detecting method is strong in sample dependency and poor in generalization ability, threshold value measurement is difficult, and judgment is not carried out in time, the method combining Hilbert-Huang transformation and statistical pattern recognition is provided, statistical modeling and clustering analysis are carried out on the time-frequency spectrum of the vibration signal based on the aggregation character of energy on frequency in the fluttering process, the characteristic parameters are utilized, the physical characteristic of cutting flutter is represented essentially, the cutting vibration state is effectively monitored in real time, and the judgment result is accurate and visual.

The invention discloses a voltage sag source identification method based on Hilbert-Huang transformation and wavelet packet energy spectra, and belongs to the field of electric energy quality analysis of electric power system. The method includes the steps: firstly, performing EMD (empirical mode decomposition) for a three-phase voltage signal to obtain IMF (intrinsic mode function) components; secondly, performing n-layer wavelet packet decomposition for the IMF components to obtain energy spectra of frequency bands; thirdly, setting a larger wavelet packet coefficient of low-frequency band energy into zero, and reconstructing wavelet packet coefficients corresponding to other frequency bands to obtain an IMF without low-frequency illusive components; finally, performing Hilbert transformation on the IMF without the illusive components to obtain an HH (Hilbert-Huang) spectrogram, and identifying voltage sag sources according to catastrophe points, amplitudes, harmonic waves and frequencies of the HH spectrogram. Different characteristics of different voltage sag sources can be identified from the HH spectrogram only by taking the amplitudes, the catastrophe points and the harmonic waves as characteristics, the voltage sag sources are differentiated, so that times of non-identification and mistaken identification are effectively decreased, and identification efficiency is improved.

The invention discloses a method for recognizing the fault type of a feed line of a power distribution line, and the method comprises the steps: obtaining waveform sampling data, and sequentially carrying out the Hilbert-Huang transformation and band-pass filtering of the waveform sampling data; reconfiguring a time frequency matrix according to the band-pass filtering data, solving the singular value of the time frequency matrix, and forming feature vector matrixes; carrying out the normalization processing of all feature vector matrixes, and enabling the feature vector matrixes to serve as the input samples of a multi-stage support vector machine after normalization processing, so as to recognize the fault type of the feed line of the power distribution line. The invention also provides a device for recognizing the fault type of the feed line of the power distribution line. The multi-stage support vector machine is good in performance, is clear in logic, is simple, and can recognize four types of power grid faults: single-phase grounding faults, two-phase grounding faults, two-phase short-circuit faults and three-phase short-circuit faults. The method provided by the invention is stronger in adaptive capability, and still has a high recognition rate of fault types under the impact of noise.

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 relates to a vibration signal feature extraction method and device, a storage medium and computer equipment. The vibration signal feature extraction method comprises the following steps:collecting a vibration signal of target equipment; carrying out noise reduction on the vibration signal, so as to obtain a noise reduction signal; carrying out empirical mode decomposition on the noise reduction signal, so as to obtain an original intrinsic mode function corresponding to the noise reduction signal; acquiring a superposed signal according to the original intrinsic mode function corresponding to the noise reduction signal, and carrying out empirical mode decomposition and superposition removal on the superposed signal, so as to obtain a final intrinsic mode function corresponding to the noise reduction signal; and carrying out Hilbert-Huang transformation on the final intrinsic mode function, so as to obtain a spectrum signature of the vibration signal. According to the vibration signal feature extraction method, the feature extraction accuracy can be increased, and extracted features can well represent the operation states of the target equipment.

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 relates to a method for diagnosing a commutation failure of an extra-high-voltage (EHV) DC power transmission system and belongs to the technical field of HVDC power transmission system fault diagnosis. The method comprises steps of: performing hierarchical reconstruction on an extracted DC current signal sequence; denoising the DC current signal sequence, filtering the DC current signal sequence with a morphological filter, and extracting the high-frequency components of the DC current signal sequence; performing Hilbert-Huang transformation on the high-frequency components, solving the instantaneous frequency maximum value f and the instantaneous frequency amplitude mean value A of an intrinsic mode function, setting a frequency threshold value f0 and a amplitude average threshold value A0, determining that the system is faulted if the f is more than or equal to f0 or that the system operates normally if not; and if the system is faulted, determining whether the A is more than or equal to A0, and determining that a line is subjected to a short-circuit fault if yes or that the system is subjected to commutation failure if not. The method may accurately determine the running state, the commutation failure, and the line short-circuit fault of the EHV DC power transmission system.

The invention discloses an integrated learning based mountain wind generation set behavior predicating model which comprises the following steps: 1, adopting a self-adaptive threshold value method todetermine a wind speed power sub-interval data density threshold value to clear abnormal data; 2, defining a sample matrix, and adopting a self-adaptive comprehensive over-sampling method to generatea new sample matrix for uniformly distributing different wind conditions; 3, performing Hilbert-Huang transform on data generated in the step 2 to obtain decomposition amount of input amount of the mountain wind generator set model; 4, according to the data of the step 4, determining input and output of the mountain wind generator set model, selecting a component learner and adopting a fusion strategy of integrated learning of stacking, and training and integrating to obtain the mountain wind generation set behavior predicating model; 5, adopting a grid search method to determine optimal parameters of the model; and 6, operating and testing the mountain wind generation set behavior predicating model. The integrated learning based mountain wind generation set behavior predicating model canprovide service for wind generation set predicating control, so that maintenance staff can normally operate a maintaining unit more efficiently better.

The invention discloses a real-time detection method for a high-speed rail injury based on vibration and audio composite signals. The method comprises the following steps: 1, installing a vibrating and acoustic sensor on a high-speed rail, forming a wireless sensor network by combining with a wireless node processor, and measuring a vibration signal and an audio signal of the rail in real time; 2, building a rail vibration model and an acoustic model by adopting a finite element method according to the actual structure of the rail, so as to obtain classic vibration and audio signals of the rail injury; 3, carrying out pretreatment on the signal by adopting Hilbert-Huang transformation; 4, building three-dimensional tensors of vibration, audio and injury varieties by fusing vibration and acoustic signals; 5, decomposing and extracting the injury variety feature coefficient by using a nonnegative tensor; 6, building an injury identification rule by using a related vector machine, and classifying real-time measurement signals, so as to determine the injury variety. By adopting the real-time detection method, real-time detection for the high-speed rail injury is achieved, and safe operation of the high-speed rail is ensured.

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 discloses a subsynchronous oscillation random time-varying mode identification method. Aiming at the problem that an error of mode identification result is relatively large due to an end effect, an improved Hilbert-Huang transformation based on a mirror image extension method is proposed to identify a random time-varying subsynchronous oscillation mode, first, symmetry points are determined at left and right ends of a signal, the signal is then extended, fitting of envelope lines adopts extreme points after extension as interpolation points to perform cubic spline interpolation,and then the local mean of the upper and lower envelope lines is calculated, so that the fitting of the upper and lower envelope lines is more accurate, the end effect is notably improved, and the feasibility and effectiveness of the method are verified by the identification of stable signals, non-stable signals and measured signals.

The invention relates to a space debris hyperspectral sequence detection method based on Hilbert-Huang transform, which belongs to the field of image processing and aims at solving the problems that the adoption of the traditional dangerous space debris detection method has to depend on sample information of an image about space debris, and the algorithm adaptability is poor, and the method comprises the following steps: 1) continuously sampling hyperspectral curves in the central position of a suspected target for T times, and processing and synthesizing the obtained T sections of the hyperspectral curves into the curve to be processed; 2) carrying out one-dimensional empirical mode decomposition and obtaining two intrinsic mode function components; 3) carrying out Hilbert transform on a two-order IMF component and obtaining the amplitude and the instantaneous frequency; 4) retaining the part which is higher than average value by one half of the amplitude, retaining the part which is higher than the average value of the instantaneous frequency, and dividing for forming a characteristics wave band set; and 5) cyclically searching the characteristic wave band set, judging whether the target rotates or not, if so, the suspected target can be determined to be the space debris, if not, the suspected target can be determined not to be the space debris.

The invention belongs to the field of information detection, and particularly relates to a two-domain multi-feature combined radiation source signal identification method, which comprises the following steps of: performing short-time Fourier transform, improved Wigner time-frequency distribution transform and Hilbert-Huang transform on a radiation source signal to obtain three time-frequency feature images; respectively extracting Green single-channel images of the three time-frequency characteristic images, and carrying out channel fusion on the three Green single-channel images to obtain a new characteristic image. According to the invention, a new three-dimensional time-frequency image is obtained by adopting a multi-time-frequency image channel fusion means. The short-time Fourier transform reflects the linear time-frequency characteristic of the signal; the improved Wigner distribution reflects the nonlinear time-frequency characteristics of signals, Hilbert-Huang transform is notrestricted by the Hessian Bourger measurement inaccuracy principle, a good time-frequency resolution is achieved, a new three-dimensional time-frequency image obtained through channel fusion can represent time-frequency information of the signals more comprehensively, and the reliability of the system is improved.

The invention discloses a method and device for identifying a fluid type of a high-pressure fluid in a pipeline during rapid pressure change, belonging to the field of fluid type identification. The device mainly comprises an incoming fluid pipeline, a fixed baffle, an incoming fluid pipeline joint, an incoming fluid sensor three-way joint, a high-pressure resistant quartz transparent tube, an outgoing fluid sensor three-way joint, an outgoing fluid pipeline joint, an outgoing fluid pipeline, an adjusting sleeve, an adjusting bolt with a through hole, an incoming fluid pressure sensor, an incoming fluid pressure transmitter, an outgoing fluid pressure sensor, an outgoing fluid pressure transmitter, a computer, a light source and a high-speed video camera. The pipeline is sealed by adjusting the screwing length of the adjusting sleeve and the adjusting bolt with the through hole. The method comprises the steps of: performing Hilbert-Huang transformation on pressure differential data to obtain each modulus energy ratio; then regarding each modulus energy ratio as input vectors of an Elman neural network to finish mapping from a characteristic space to a fluid type space. The method and device for identifying the fluid type of the high-pressure fluid in the pipeline during the rapid pressure change, disclosed by the invention, has advantages of simple structure, convenience in detachment and capability of identifying fluid type of high-pressure fluid in the pipeline rapidly and exactly during the rapid pressure change.

The invention discloses an OPGW lightning strike site polarization state waveform signal processing method. According to the method, a polarization demodulation device is adopted to acquire OPGW lightning strike site polarization state signals, and a wavelet denoising method is adopted to perform denoising preprocessing on sampling signals, so that wavelet denoised polarization state signals x(t)can be obtained; EMD decomposition is performed on the signals x(t), an equation described in the descriptions in the invention is obtained, rn in the equation is called a residual function, and ci inthe equation is the i-th component of the signals x(t) that satisfies an IMF condition; HHT (Hilbert-Huang) transformation is performed on each intrinsic mode function ci(t) of the polarization statesignals, and then analytical signals are constructed, and a lightning strike point can be accurately located on the basis of optical polarization state signals extracted by the final Hilbert-Huang transformation; and therefore, the problem of low accuracy of the analysis and processing of optical polarization state signals in an OPGW in the prior art can be solved, and the problem of inaccurate fault location caused by the low accuracy, and other problems can be also solved.

The invention relates to ultra-wide band microwave imaging, ultra-wide band wireless detection, biomedical detection equipment and microwave detection, and in particular to a rapid imaging method for ultra-wide band microwave detection based on Hilbert-huang transformation. According to the method, biological information can be displayed simply, conveniently, rapidly and obviously. According to the technical scheme, the rapid imaging method includes the following steps that an antenna Ai is used for emitting ultra-wide band microwave signals, and other antennas are used for receiving reflecting signals from the inner portion of one breast so as to obtain signals containing tumor information and signals containing no tumor information; tumor response signals are obtained by subtracting the signals containing no tumor information from the signals containing the tumor information, Hilbert transform is conducted on all the obtained tumor response signals, the signals are constructed into analytical signals, and then the instantaneous amplitude of the signals is obtained; for a breast area, scanning is conducted on each point in an imaging area through a confocal imaging method, and the position and size information of a tumor is obtained clearly. The rapid imaging method is mainly used for designing and manufacturing medical apparatuses and instruments.

The invention discloses a manifold learning and Hilbert-Huang transformation combined structural modal parameter identification method. The method comprises the following steps of: 1, acquiring time domain response data of a measure point in a structure; 2, processing the time domain response data acquired in the step 1 by adoption of a manifold learning algorithm so as to obtain a vibration modeand a fixed frequency of the structure; and 3, processing the time domain response data acquired in the step 1 by adoption of a Hilbert-Huang transformation method so as to obtain a damping ratio of the structure. Compared with the prior art, the method has the beneficial effects as follows: 1, when modal parameter extraction is carried out by utilizing the manifold learning and Hilbert-Huang transformation combined method, vibration modes, fixed frequencies and damping ratios with relatively high precision can be obtained through response data when material parameters and experiment conditions of structures are unknown; and 2, the method can be used for processing nonlinear data and retaining nonlinear manifolds of the structures.

The invention relates to an HHT harmonic analysis method based on improved EMD modal aliasing and endpoint effect, and provides an improved HHT harmonic analysis method aiming at the problems that thedecomposition of the Hilbert-Huang Transform (HHT) method for analyzing power harmonic signals at present is easily interfered, the result is inaccurate and the like. According to the method, the harmonic signal of the power system is preprocessed based on the B-spline least square fitting method, the influence of an intermittent event or noise interference on the signal is well weakened, and themodal aliasing phenomenon occurring in classical mode analysis (EMD) decomposition is inhibited; and a slope matching waveform continuation method is adopted for the pre-processed fitting curve to ensure that the change trend at the signal endpoint is consistent with the inside and ensure the integrity of the signal, so that the endpoint effect of EMD decomposition is weakened and even inhibited,and the EMD decomposition result is improved. Therefore, the accuracy of decomposing the harmonic signal of the power system is improved, the harmonic composition in the power harmonic system is rapidly detected, and the application of harmonic detection is expanded.