240 results about "Continuous wavelet" patented technology

This invention pertains to the extraction of the slowness dispersion characteristics of acoustic waves received by an array of two or more sensors by the application of a continuous wavelet transform on the received array waveforms (data). This produces a time-frequency map of the data for each sensor that facilitates the separation of the propagating components thereon. Two different methods are described to achieve the dispersion extraction by exploiting the time frequency localization of the propagating mode and the continuity of the dispersion curve as a function of frequency. The first method uses some features on the modulus map such as the peak to determine the time locus of the energy of each mode as a function of frequency. The second method uses a new modified Radon transform applied to the coefficients of the time frequency representation of the waveform traces received by the aforementioned sensors. Both methods are appropriate for automated extraction of the dispersion estimates from the data without the need for expert user input or supervision

The invention discloses a wavelet transformation and deep learning-based rolling bearing weak fault diagnosis method. The method comprises the following steps: a rolling bearing vibration signal is obtained, and the acquired vibration signal is subjected to continuous wavelet transformation to obtain a time-frequency diagram; autocorrelation operation is performed on wavelet coefficients corresponding to each frequency on the time-frequency diagram to filter out noise interference and extract periodic fault components; the Hilbert transformation is used to perform envelope demodulation to obtain a fault characteristic frequency; a processed time-frequency diagram is input as a feature diagram, and categories of early faults are determined by training a deep learning classification model. Theoretical and experimental results show that fault categories can be determined accurately at an early stage when weak fault of a rolling bearing occur based on an improved wavelet time-frequency diagram used as the classification model for input training, right determination results can be given when the method is applied to different bearings, high training speed can be realized, and high actual application value can be achieved.

The invention relates to a self-adaptive failure diagnosis method of a rotary mechanical component based on continuous wavelet transformation. The method comprises the following steps of: 1, performing zero-mean preprocessing on an acquired discrete initial vibration signal to obtain a preprocessed signal from which a direct-current component is eliminated; 2, performing continuous wavelet transformation on the preprocessed signal obtained in the step 1 to obtain a wavelet coefficient which corresponds to each scale parameter; 3, calculating the kurtosis of the wavelet coefficient which corresponds to each scale parameter in the step 2 respectively; 4, searching for a wavelet scale parameter which corresponds to large kurtosis in the wavelet scale parameter obtained in the step 3 with a self-adaptive algorithm to configure an optimal analysis signal; and 5, performing envelope demodulation on the optimal analysis signal obtained in the step 4 to obtain an envelope signal. The method has the beneficial effects that: the accuracy of failure diagnosis is increased, and the method is particularly suitable for failure diagnosis of mechanical parts with impact damages.

The invention discloses an ultrasonic detecting method of defects in a thick-wall composite tubular structure. The ultrasonic detecting method includes the steps that original signal data are obtained, final signal data are obtained with signal processing methods such as noise reduction, fast Fourier transforming and continuous wavelet transforming, and a wavelet-coefficient-amplitude-time graph is generated; according to the generated wavelet-coefficient-amplitude-time graph, the positions and the sizes of inside damage of a composite material layer and the positions and the sizes of the unsticking portions of the boundary between the composite material layer and a metal layer are extracted, and distribution of the thickness of the metal layer is calculated. The ultrasonic detecting method can be used for detecting the multiple defects of the tubular structure composed of composite materials and metal, damage positioning and size assessing are carried out through the corresponding signal processing technology, and quantitative analysis of the whole structure performance can be achieved; the ultrasonic detecting method is high in accuracy, free of reference signals, simple, easy to operate and capable of being used for field detecting.

The invention discloses a rotary machine instantaneous rotation speed estimation method based on vibration signal synchronous compression transformation. The rotary machine instantaneous rotation speed estimation method comprises the following steps of 1 obtaining a vibration signal in the rotary machine operation process; 2 conducting frequency shift treatment on the measured vibration signal, 3 conducting synchronous compression continuous wavelet transformation on the vibration signal subjected to the frequency shift treatment to obtain the time-frequency distribution of the vibration signal subjected to the frequency shift treatment, 4 utilizing a Viterbi algorithm to extract first-order instantaneous frequency components of the vibration signal subjected to the frequency shift treatment from the obtain time-frequency distribution, and 5 utilizing the extracted first-order instantaneous frequency to recover calculation so as to obtain the rotary machine instantaneous rotation speed. The rotary machine instantaneous rotation speed estimation method adopts a frequency shift algorithm and the synchronous compression continuous wavelet transformation to process the signal, achieves accurate estimation of the instantaneous frequency of the vibration signal, utilizes the Viterbi algorithm to achieve accurate extraction of the instantaneous frequency and can accurately extract the instantaneous rotation speed of a rotary machine which cannot directly measure the instantaneous rotation speed through the vibration signal.

The invention relates to a winter wheat area estimation method based on remote-sensing time series data, which is based on the remote-sensing time series data, and comprises the steps that remote-sensing vegetation index time series data is converted into a wavelet coefficient spectrum by continuous wavelet conversion; wavelet coefficient binary images representing annual vegetation change characteristics of pixels in a study region are established on the basis; the wavelet coefficient binary images of a plurality of known winter wheat sampling points are superposed; standard wavelet coefficient binary images of winter wheat in the study region are generated; the wavelet coefficient binary images of the pixels in the study region and the standard wavelet coefficient binary images of the winter wheat are superposed one by one; winter wheat identification criteria are established after statistic superposition; the pixels are subjected to winter wheat identification one by one; and finally a winter wheat planting area of the whole study region is obtained by summarizing and computing. The method can effectively solve the problems that annual variance amplitude of the original vegetation index is inconsistent due to various factors, and has the advantages of high anti-noise capacity, good classification accuracy, wide application scope and the like.

The invention relates to a method for inverting vegetation parameters by remote sensing based on reflection spectrum wavelet transform. The method comprises the following steps of: 1) acquiring the vegetation parameters and the original spectrum thereof under different conditions, and performing spectrum transform on the original spectrum; 2) performing continuous wavelet transform on the original spectrum by using different wavelet functions, and generating wavelet coefficients with different frequencies; 3) performing stepwise regression by taking different scales of wavelet coefficients as independent variables and taking the vegetation parameters as dependent variables, selecting spectrum wave bands needed by the inversion of the vegetation parameters, constructing a model of quantitative inversion of the vegetation parameters, and calculating R2 of the model; and 4) comparing modeling R2 of the constructed model according to different wavelet decomposition scales, and determining the model with the maximum modeling R2 as the optimal model. By the method, the hyperspectral remote sensing inversion precision of the vegetation parameters can be obviously improved, and the remote sensing inversion precision of biochemical parameters can be improved preferably. The method has wide parameter applicability, is applicable to leaf or canopy reflection spectrum, and is applicable to satellite remote sensing hyperspectral data.

Slowness dispersion characteristics of multiple possibly interfering signals in broadband acoustic waves as received by an array of two or more sensors are extracted without using a physical model. The problem of dispersion extraction is mapped to the problem of reconstructing signals having a sparse representation in an appropriately chosen over-complete dictionary of basis elements. A sparsity penalized signal reconstruction algorithm is described where the sparsity constraints are implemented by imposing a l₁ norm type penalty. The candidate modes that are extracted are consolidated by means of a clustering algorithm to extract phase and group slowness estimates at a number of frequencies which are then used to reconstruct the desired dispersion curves. These estimates can be further refined by building time domain propagators when signals are known to be time compact, such as by using the continuous wavelet transform.

The embodiment of the invention discloses a fault detection method and fault detection apparatus used for a shafting device, relating to the automatic control technical field, and can detect and discriminate minimal fault forms, and increase fault prediction accuracy. The method comprises: obtaining operation signals of a shafting device in a ship when the ship sails smoothly, the operation signals comprising displacement, speed and acceleration signals of the bearing, axle tube and coupler in the shafting device in vertical and horizontal directions; performing frequency domain main energy extraction on the operation signals, generating reconstructed signals, performing continuous wavelet decomposition on the reconstructed signals, and generating a wavelet grey-scale map based on the result of continuous wavelet decomposition; performing feature extraction on the wavelet grey-scale map, removing redundancy feature vectors with relevance, and generating a fault characteristic vector set according to kept characteristic vectors; and determining whether the shafting device has faults in dependence on the fault characteristic vector set. The fault detection method and fault detection apparatus are suitable for shafting device fault detection.

The invention provides a laser radar echo signal denoising method based on synchronous compression conversion, and belongs to the technical field of laser data processing. The method includes firstly,taking a noisy laser echo signal as an observation signal and a transmitted laser pulse signal as a reference signal to carry out continuous wavelet transformation to obtain a wavelet time frequencyspectrum; secondly, taking the latter as a template and the former as a target image to carry out image matching and finding the registration time position; thirdly, performing further synchronous compression conversion on the two signals, adjusting the frequency spectrum position when the reference signal is synchronously compressed according to the registration time position, and determining theenvelope of the ridge line at the moment; fourthly, when the observation signal are synchronously compressed, extracting the frequency spectrum according to the ridge line envelope range, taking theenvelope range as a time-frequency range of the effective component of the observation signal, carrying out the signal reconstruction to obtain a denoised result of the laser radar echo signal. The echo pulse signal can be extracted with high quality, and the detection distance and ranging accuracy of the laser radar are improved.

The invention discloses a nonlinear ultrasonic mixing method for testing in the structural fatigue crack direction, and belongs to the field of non-destructive testing. The method comprises the following steps: firstly, according to a tested object and a nonlinear ultrasonic mixing harmonic condition, determining the testing parameters such as the mode, frequency, incident angle and the like of two fundamental frequency waves; performing the nonlinear ultrasonic mixing testing of the structural fatigue crack; performing continuous wavelet transformation on testing signals in each annular receiving point, extracting the amplitudes of mixing waves, and performing the mixing sound field directivity analysis; finally, calculating the fatigue crack direction according to the change rule of thereflecting mixing wave propagation direction along with the fatigue crack direction.

The invention relates to a fault range finding method for a power transmission circuit by use of current travelling waves and voltage travelling waves of two ends of the circuit, and belongs to the technical field of fault range finding of electric power systems. When a power supply circuit fails, current travelling waves and voltage travelling waves of the circuit are acquired from two measurement points of the circuit. Reflection wave head recognition is performed on the current travelling waves and the voltage travelling waves of the two ends of the circuit through continuous wavelet transformation and four groups of fault distance data is acquired according to initial travelling wave heads and emission wave heads. Composite integration is performed on the four groups of the data by use of the global geometrical average optimal method, so fault range finding of the power transmission circuit is achieved. According to the invention, accuracy of fault range finding of the power transmission circuit can be effectively improved and the method is quite highly reliable.

The invention discloses a method for comprehensively recognizing sedimentation hiatal surfaces in an ultra-thick seam. The sedimentation hiatal surfaces in the ultra-thick seam are comprehensively recognized according to five parameters acquired through coal petrography and coal quality analysis, microelement analysis, organic matter stable carbon isotope analysis, one-dimensional continuous wavelet transformation of log data and Milankovitch cycle analysis of the log data; growth control factors of the sedimentation hiatal surfaces are explained from the origin essentially through recognition of Milankovitch cycles in the ultra-thick seam, the root cause of growth of the sedimentation hiatal surfaces is found out, and the accuracy and the credibility of the sedimentation hiatal surfaces are greatly improved. Besides, weight scoring is performed according to sensitivity of different recognition methods, the recognized sedimentation hiatal surfaces are correspondingly assigned, the scores obtained with all the methods are added up, and the reliability of the sedimentation hiatal surfaces is described quantitatively. The recognition method is convenient and flexible and has better practicability.

The invention finds a method capable of simultaneously finishing LC/MS or LC signal base line correction, peak position distinguish and peak position alignment. The invention mainly comprises the following steps: step 1, correcting a metabolic spectrum base line and distinguishing peak position, wherein the main process comprises: 1, within the range of 1-12 feet, taking the Mexican hat function as a mother wavelet, and carrying out continuous wavelet transform (CWT) of which the step length is 1 to each mass spectrum signal; 2, at each time point, detecting the maximum value of the wavelet transform of each mass spectrum signal on each scale to obtain a new signal of which the horizontal coordinate is time and the vertical coordinate is the maximum value of the wavelet transform; and step 2, aligning the peak position. A threshold value (the maximum deviation of the peak position of the same component in different samples) is given, the peak of the deviation in the wavelet transform signal in each sample within the threshold value is incorporated into the same peak, and the corresponding peak position in each sample is modified.

The invention relates to a pipe pressure anomaly diagnostic method based on combined filtering and dynamic thresholds. The pipe pressure anomaly diagnostic method includes the steps of obtaining pressure signals of liquid to the head end and the tail end of a pipe in real time, carrying out decomposition reconstruction through low-pass filtering and discrete wavelet filtering, obtaining pressure signals generated after combined filtering, carrying out segment treatment on the pressure signals, calculating the dynamic thresholds of the segmented pressure signals obtained after combined filtering, judging whether the pressure signals of the pipe are abnormal or not in real time through the dynamic thresholds, judging whether the pressure anomaly signals at the head end and the tail end of the pipe are pressure anomaly signals caused by a same source or not with the Pearson related coefficient method, carrying out continuous wavelet transformation on the pressure anomaly signals with the same source to obtain moments corresponding to the maximum value points of wavelet coefficients, and carrying out anomaly point positioning through the difference of the moments corresponding to the maximum value points. By means of the pipe pressure anomaly diagnostic method, the pipe anomaly diagnostic sensitivity is improved, the misjudgment rate is reduced, and pressure anomaly points are accurately detected and positioned in the shortest time.

A method for extracting data from an overall signal, generated by a plurality of impulses, by use of a computer using wavelet transform analysis of timing and strength of each of said plurality of impulses is disclosed. The method includes using a Discrete Wavelet Transform analysis or a Continuous Wavelet Transform analysis.

The invention discloses a seismic data time-frequency analysis method based on second-order extrusion wavelet transformation. Firstly, a second-order synchronous extrusion operator is introduced on the basis of continuous wavelet transformation, the transformation is rearranged in a time-scale domain according to wavelet transformation coefficients, accordingly energy diffusion is pressed, and analysis results higher in time-frequency resolution ratio can be obtained by adopting the method. Compared with existing synchronous extrusion transformation, the method is more suitable for analysis of seismic signals having stronger frequency modulation, thus the signals are applied to time-frequency analysis of actual seismic data, the boundary of an underground carbon and hydrogen reservoir can be more accurately defined, riverways, faults and other geological information can be more elaborately depicted, and accordingly further seismic data explanation and well position determination are promoted.

The invention discloses a complex analytic optimal wavelet demodulation method which is characterized by comprising the following steps of: obtaining a vibration signal spectrogram, and capturing an effective frequency band centralized region; setting a shape factor sigma of a Morlet wavelet and an initial value of the initial frequency omega 0, thereby obtaining the wavelet factor Cs(b, a); obtaining continuous wavelet amplitude value spectrum information measure SH; improving the wavelet kurtosis; calculating the smooth index; calculating the optimal wavelet envelope spectrum; and detailing a modulation spectrum, and locating the fault of a detected target. According to the invention, not only are analysis wavelet parameters optimized and selected, but also the conventional wavelet kurtosis calculation method is improved, and the accuracy of screening the wavelet coefficients is improved; meanwhile, in order to solve the problem that the wavelet coefficient features decomposed by fault signals at the early stage are not obvious, the smooth index is additionally set, which plays better positioning and screening effects on the wavelet coefficients for the fault frequency band. The method not only can disclose weak fault features in complex signals in the multi-component signals accurately and effectively, and has high practical value.

Methods and systems are provided for analyzing a physiological signal by applying a continuous wavelet transform on the signal and comparing the wavelet transformation to a library of wavelet signatures corresponding to one or more physiological conditions and/or patient conditions. A pulse oximeter system may relate the wavelet transformation with one or more of the wavelet signatures based on filters and/or thresholds, and may determine that the wavelet transformation indicates that the patient of the physiological signal has a physiological condition indicated by the related wavelet signature. In some embodiments, the pulse oximeter system may use previous analyses in a neural network to update the library. Further, non-physiological components of the wavelet transformation may also be identified and removed.

The invention relates to an evaluation method of influence of an optical element surface waviness on a laser damage threshold and a method for obtaining optimal processing parameters of the element therefrom, in particular to a method for evaluating surface quality of an optical element and a method for obtaining the optimal processing parameters of the element. The evaluation method comprises the following steps of obtaining a morphology data matrix of an original processing surface; obtaining a relative laser damage threshold relevant to each characteristic frequency by utilizing a power spectrum density method, a two-dimensional continuous wavelet transformation method and a fourier modulus method; and selecting a minimum value as an evaluation result. In the method for obtaining the optimal processing parameters of the optical element, the optimal processing parameters are obtained by utilizing the evaluation method and comparing the relative laser damage thresholds of thr optical element obtained under various processing parameter conditions. The invention can be used for evaluating the quality of the optical element and can also be used for guiding the processing process of the optical element.

The invention relates to a method for extracting the time-frequency amplitude characteristic and the time-frequency phase characteristic of ultrasonic signals on a dissimilar material diffusion welding interface, belonging to the filed of the nondestructive detection and aiming at solving the problem that the conventional method for detecting the diffusion welding quality according to the amplitude of the reflection echo on the diffusion welding interface can not accurately reflect the information on the diffusion welding defect. The method comprises the following steps: collecting ultrasonic signals from the dissimilar material diffusion welding interface; collecting reference signals from a reference sample; continuously changing the wavelets of the ultrasonic signals collected from the dissimilar material diffusion welding interface and the reference signals collected from the reference sample by using the parameter-optimized complex Morlet wavelets to obtain the ratio (R) (a, b) of the ultrasonic signals and the reference signals; respectively calculating the time-frequency amplitude |R (a, b)| and the time-frequency phase Phi (a, b) of the ultrasonic signals collected from the dissimilar material diffusion welding interface by using the R (a, b); respectively calculating the time-frequency amplitude characteristic value CR and the time-frequency phase characteristic value CPhi by using the time-frequency amplitude |R (a, b)| and the time-frequency phase Phi (a, b); and reconstructing the time-frequency amplitude characteristic value CR image and the time-frequency phase characteristic value CPhi image of the ultrasonic signals collected from the dissimilar material diffusion welding interface. The invention is suitable for detecting and evaluating the quality of the diffusion welding interface.