103 results about "Marginal spectrum" patented technology

The invention provides a heartbeat frequency detection algorithm of a non-contact vital sign detection system; the steps are: respectively doing bandpass filtering for I and Q two way signals outputted by a continuous wave doppler radar; using a center estimate algorithm to carry out useful dc component recovery; using a complete cluster experience modal decomposition algorithm to separate a heartbeat signal from a human body jitter signal, a breathing signal and environment interference noises; resolving a Hilbert marginal spectrum of each decomposition signal and doing peak value detection; finding out the Hilbert marginal spectrum corresponding to the heartbeat signal according to a marginal spectrum peak position and energy concentration degree close to the spectrum peak; obtaining heartbeat frequency information according to the spectrum peak position. The heartbeat frequency detection algorithm can effectively extract the heartbeat signal under unstable human body and large environment interference noises, thus obtaining accurate heartbeat frequency information, improving anti-interference property of the non-contact vital sign detection system, and satisfying heartbeat frequency detection accuracy demands of medical affairs personnel.

The invention discloses a time-varying non-stable-signal time-frequency analyzing method. The method comprises the steps of providing definitions of single-frequency signals and i(th)-level local extremum, conducting single-frequency signal decomposition on the time-varying non-stable signals according to the definitions so as to obtain a plurality of single-frequency components, then obtaining a plurality of instantaneous frequency and instantaneous amplitude of the components by Hilbert, further, drawing the amplitude on a time-frequency plane in an amplitude-frequency-time three-dimensional space to obtain a Hilbert amplitude spectrum, and conducting integration on the time through the amplitude spectrum to obtain a Hilbert marginal spectrum. According to the method, the complete adaptability is realized; meanwhile, the method is not restricted by uncertainty principle, and the time and the frequency have high resolution ratio; the decomposition is thorough, so that the respectively independent single-frequency components, namely the substantive characteristics, are obtained, and cross terms among all the components do not exist; and the calculating time is saved as the accelerating method is adopted in the decomposition process.

The invention provides a method for recognizing over-voltage fault types of a power distribution network. The method comprises the following steps: acquiring zero sequence voltage waveform sampling data x(t); taking an IMF (Intermediate Frequency) signal with a maximum correlation coefficient C as a principle mode component y(t); calculating a marginal spectrum h(omega) of the y(t); judging that a frequency dividing resonance fault occurs when an energy ratio of a frequency band of 10-40Hz in h(omega) exceeds 50%; judging that a high-frequency resonance fault occurs when an energy ratio of a frequency band of 100-150Hz in h(omega) exceeds 50%; otherwise calculating overall similarity S of the x(t) and standard single-phase earth fault zero sequence voltage waveform; judging that a fundamental resonance fault occurs when S is smaller than a preset similarity threshold K, otherwise judging that a single-phase earth fault occurs. The method disclosed by the invention is capable of automatically recognizing the over-voltage resonance type and the single-phase earth fault, simple in algorithm, less in calculation time, high in anti-jamming capability and high in recognition accuracy.

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 an early fault determining method for a bearing. The method comprises the steps of cutting an acquired bearing vibration time domain signal into N groups of secondary vibration signals according to the same length; 2, calculating local spectral band energy Mg of each secondary vibration signal, namely, 1) performing frequency domain conversion for each secondary vibration signal; 2) selecting a local spectral band from the whole Hilbert marginal spectrum based on the formula shown in specification as the interval, wherein fp is the bearing fault characteristic frequency calculated based on the bearing structural dimension, and delta f is 2Hz; 3) calculating the local spectral band energy through the formula shown in the specification, wherein h(f) is frequency amplitude in the local spectral band; 3, creating a local spectral band energy sequence through N Mg, wherein the local spectral band energy value of each vibration signal is Mf when the bearing is free of a fault; Mg is not less than the product of K and Mf at M times in the sequence, wherein K is a constant; the characteristic power rate (CPR) is determined according the formula shown in the specification; 4, determining the early fault when the CPR is more than or equal to some constant A.

The invention discloses a wheel polygon identification method based on the wheel-rail vertical force and a device thereof. The identification method comprises the following steps: collecting the wheel-rail vertical force signal by the shear stress method; and analyzing the collected wheel-rail vertical force signal Carry out the EEMD decomposition of the ensemble empirical mode to obtain the aggregated intrinsic mode function IMF component; calculate the marginal spectrum of the intrinsic mode function IMF containing the wheel polygonal disease characteristic information; according to the obtained marginal spectrum of the intrinsic mode function IMF, obtain The damage characteristic frequency f is used to identify whether the wheel has polygons and the type of polygons according to the preset judgment criteria. Its advantages are: it can carry out real-time monitoring and damage evaluation on the vehicles passing through the monitoring section, and it has the characteristics of fast and accurate; The marginal spectrum of the wheel-rail vertical force damage characteristic frequency after polygonal damage of the wheel is obtained.

The invention relates to a high speed railway rail corrugation acoustic diagnosis method based on an IMF (Intrinsic Mode Function) energy ratio, and belongs to the technical field of high speed railway vibration noise. The method comprises the following steps that: (1) testing rail roughness; (2) carrying out ensemble empirical mode decomposition; and (3) obtaining an IMF energy ratio: according to the energy ratio of an IMF signal corresponding to fault characteristic frequency, carrying out rail corrugation fault identification, screening to obtain an IMF component corresponding to the railcorrugation, and obtaining a Hilbert marginal spectrum and instantaneous frequency through HHT (Hilbert-Huang Transform). By use of the high speed railway rail corrugation acoustic diagnosis method based on the IMF energy ratio, a direct method is adopted on an operation line to actually measure the rail roughness characteristics of a corrugation-free zone, the IMF energy ratio obtained after an acoustic signal is subjected to EEMD (Ensemble Empirical Mode Decomposition) is subjected to component screening, and IMF energy ratio distortion characteristics are used for carrying out rail corrugation identification. Compared with theoretical acoustic frequency corresponding to the rail roughness actually measured by the direct method, the invention puts forward an efficient high speed railwayrail corrugation acoustic diagnosis strategy.

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 relates a method for analyzing a time-intensity curve of a myocardium ultrasonic angiography image, in particular to a method for measuring myocardium ultrasonic angiography image physiological parameters based on EMD. The method includes dividing a myocardial area into six areas according to a circumference, extracting perfusion signals and extracting a time-intensity curve of each of divided myocardial areas respectively; performing EMD on an extracted time-average gray value intensity curve to obtain an expanded signal intrinsic mode function, performing Hilbert transform on the obtained first intrinsic mode function to obtain instantaneous spectrum parameters, and adding amplitude values of each frequency point to obtain a marginal spectrum; and performing threshold judgment on a marginal spectrum through energy thresholds of the marginal spectrum. According to the method, an analytical approach based on the myocardium ultrasonic angiography image is provided for diagnosing myocardial infarction or myocardial microcirculation, and further physiological parameter measurement accuracy based on the myocardium ultrasonic angiography image is improved, and doctor subjective dependence is reduced.

The present invention provides a method and device for extracting a vibration signal characteristic frequency band. The method comprises a step of extracting the marginal spectrum of a vibration signal, a step of dividing the marginal spectrum into a plurality of window marginal spectrums by using a sliding window and carrying out clustering analysis on the window marginal spectrum sets under the same frequency band in different fault states so as to calculate and generate the clustering effect evaluation index of each frequency band window marginal spectrum set, and a step of extracting a fault sensitive characteristic frequency band according to the clustering effect evaluation index. The vibration signal characteristic frequency band extracted by the method and device of the invention has the advantages of high fault recognition rate and good noise immunity ability quality.

The invention provides a vibration type flow meter characteristic signal extraction method based on improved assemble average empirical mode decomposition. The method includes the steps of conducting end continuation processing on a collected vibration type flow meter flow vibration signal through a waveform-matched self-adaption end continuation method, conducting envelope line fitting on the collected vibration signal through a cubic B-spline method, conducting MEEMD decomposition to obtain a plurality of IMF components, conducting relevance analysis on the IMF components and the original signal, selecting the useful IMF components, conducting HHT conversion on the IMF components, and obtaining the Hilbert time-frequency spectrum and the marginal spectrum of the flow signal, wherein the Hilbert time-frequency spectrum and the marginal spectrum are the signal characteristics of the vibration type flow meter flow vibration signal. The method is suitable for accurately and rapidly metering the pipe network fluid flow in the industrial field.

The invention provides a rolling bearing fault diagnosis method based on optimized variational mode decomposition, and the method comprises the steps: selecting 4096 sampling points of an original vibration signal as input signals of variational mode decomposition; optimizing the modal number and the secondary penalty factor of variational modal decomposition by adopting an improved bat algorithmand taking the minimum average envelope entropy as an optimization target; decomposing the original vibration signal by using the optimized parameters, and solving an energy entropy and an energy spectrum entropy of a decomposed component; taking the kurtosis, the correlation coefficient and the marginal spectrum entropy as screening criteria to screen the components, and solving main frequency distribution characteristics of the reserved components; and taking the energy entropy, the energy spectrum entropy and the main frequency distribution characteristics as characteristic vectors and inputting the characteristic vectors into a support vector machine so as to realize fault diagnosis. According to the method, the variational mode decomposition parameters are optimized through the improved bat algorithm, and the feature vectors are obtained according to the optimized parameters, so that manual parameter determination is avoided, the optimal solution can be found more quickly, and therecognition rate of the fault state is improved.

The invention discloses a speech emotion recognition method based on variational modal decomposition and an extreme learning machine, and belongs to the field of artificial intelligence and speech recognition. The speech emotion recognition method first preprocesses an emotional speech signal through a variational modal decomposition method, the emotional speech signal is decomposed into a plurality of intrinsic mode function (IMF) components and a residual component, and the components can reflect the change of a original sequence more accurately and retain the emotional characteristics of the speech signals; then, the IMF components are subjected to hilbert conversion to obtain hillbert marginal spectrum characteristics of the IMF components; and in addition, the IMF components are reaggregated to obtain the speech signal removing the residual component, and then an MEL cepstrum function is extracted for the signal. The extracted new characteristics are added into a traditional speech emotional characteristic set, and an extreme learning machine model is constructed for classification and recognition. The speech emotion recognition method has the advantage of obtaining new speechcharacteristics through the variational modal decomposition. Compared with the traditional speech emotional characteristics, the characteristics have a higher recognition rate for speech emotion recognition.

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 invention discloses a detection method of a steel pipe concrete cavity defect extracted on the basis of HHT characteristics. The method comprises the following steps: (1) applying mechanical impact on a detection point on the surface of steel pipe concrete to excite stress waves, using a sensor to receive the stress wave detection signals; (2) adopting an HHT algorithm to extract cavity characteristic signals from the stress wave detection signals to obtain a characteristic Hilbert marginal spectrum; (3) changing the detection point, repeating step (1) and (2); subjecting the characteristic Hilbert marginal spectrums corresponding to different detection points to an image wave train display; and judging the size of cavity of steel pipe concrete according to the characteristic Hilbert marginal spectrum. By accumulatively displaying the images of characteristic Hilbert marginal spectrums of multiple detection points, the situation of cavity defects in steel pipe concrete can be directly display and calculated, and the method has the advantages of strong reliability, and accurate detection result.

The invention discloses a power line two-way power-frequency communication uplink signal detecting method. The method is characterized by including the steps of firstly, making background power-frequency signals of modulating signals cancelled out and enhancing the modulating signals through a weighted summation method in the two-way power-frequency communication uplink signal detecting process, conducting derivation on the enhanced modulating signals, and conducting empirical mode decomposition on the modulating signals through ensemble empirical mode decomposition; secondly, finding the order, where the frequency of 150 HZ to 500 HZ is located, in an n-order intrinsic mode function through correlation operation, finding the threshold value of a corresponding order through the intrinsic mode function of the order where the frequency of 150 HZ to 500 HZ is located, conducting threshold value denoising, and calculating the Hilbert marginal spectrum of the denoised signals through Hilbert conversion, and therefore the time domain where the modulating signals are located is detected, most noise and harmonic interference can be effectively removed, and the time domain where the modulating signals are located can be accurately detected. The method has the advantages of being scientific, reasonable, easy to implement, high in detection accuracy and the like.

The invention provides an underwater maneuvering small target recognition method based on the HHT and an artificial neural network. The method includes the steps of extracting a HHT feature of a signal x(t) to be recognized: a Hilbert marginal spectrum, and extracting a HHT feature quantity from the Hilbert marginal spectrum; subjecting the signal x(t) to be recognized to Fourier transform, and extracting a peak frequency and 3dB bandwidth thereof; mixing the HHT feature quantity, the peak frequency and the 3dB bandwidth to construct a HHT mixed feature vector; and finally, inputting the HHT mixed feature vector into a trained artificial neural network classifier for recognition, and outputting the recognized type. The method of the invention makes full use of the adaptability of the HHT to signals, the advantages of processing non-stationary signals and the characteristics of the time-frequency domain of underwater small target radiated noise signals, and the extracted high-dimensional feature quantity can fully describe the features of an underwater target; and the recognition rate of the underwater maneuvering small target is improved.