77 results about "Generalized s transform" patented technology

The invention relates to a seismic wave absorption and attenuation compensation method, and belongs to the geophysical exploration data processing technology. The method comprises the following steps: selecting one of VSP (vertical seismic profiling) initial data as reference data, carrying out generalized S-transform on the reference data, selecting sampling points as reference points, and recording the time and time-frequency spectrums of the sampling points; carrying out generalized S-transform on data of each sampling point, and dividing the time-frequency spectrum of corresponding frequency in the time-frequency spectrums of the reference points by the time-frequency spectrum of each sampling point so as to obtain an absorption and attenuation curve; determining a natural logarithm to figure out a phase compensation operator according to a formula of frequency difference of absorption and attenuation signals in a time frequency domain; and carrying out absorption and attenuation compensation and phase compensation in the time frequency domain to obtain a fine VSP wave field profile. The method has the advantages that noise can be eliminated during the process of fitting the absorption and attenuation curve; generalized S-inverse transform without energy loss is realized; signal-to-noise ratio and resolution of the data are improved after compensation; log resolution control is realized; the reference points are selected flexibly; and algorithm is simple and efficient.

The invention provides a fault diagnosis method of a variable-speed rotation machine based on time-frequency spectrum segmentation. The method includes following steps: step 1, obtaining normalized time-frequency spectrums of signals through multi-resolution generalized S transform, and generating multi-resolution binarized time frequency spectrums; step 2, combining the binarized time-frequency spectrums with all resolutions, and obtaining an optimal binarized time-frequency spectrum; step 3, segmenting the optimal binarized time-frequency spectrum into a plurality of connected domains, and performing information annotation on each connected domain; step 4, extracting an optimal expression atom of each connected domain, forming an optimal atom set, and calculating the expression of a fault signal in the optimal atom set; and step 5, calculating the appearance time and amplitude of an impact theory, and realizing fault diagnosis of variable-sped mechanical equipment through informationcomparison. According to the method, most strong background noises can be filtered, and fault diagnosis of impact type faults including cracking, pitting corrosion or spalling etc. of the variable-speed rotation machine can be realized.

The invention discloses a seismic spectrum imaging method based on deconvolution generalized S transform, which comprises the steps that a generalized S transform spectrum is acquired by performing two-dimensional convolution on Wigner distribution of original signals and a Gaussian window, a transform spectrum is acquired by performing generalized S transform on seismic data, and time-frequency distribution of the original signal can be acquired through deconvolution when the generalized S transform spectrum and the Wigner distribution of a window function are known. The seismic spectrum imaging method combines advantages of generalized S transform and Wigner-Ville distribution, generation of a cross term of the Wigner-Ville distribution is effectively suppressed through a generalized S transform window, and the generalized S transform spectrum is enabled to acquire high time-frequency aggregation at the same time; and deconvolution generalized S transform can adaptively adjust an analysis window along with variations of a frequency component, is applicable to time-frequency analysis for unstable seismic data, and can acquire high time-frequency resolution; and the seismic spectrum imaging method is applied to detecting the oil-gas possibility of a reservoir, thereby being conducive to improving the reservoir prediction accuracy.

The invention relates to a weak object detection method on the basis of generalized S transformation, which pertains to the field of image processing, in particular to a weak object detection method by applying the two-dimensional generalized S transformation of an image. The method carries out two-dimensional generalized S transformation to the original image I (x', y') at first to obtain a generalized S transformation result S (x, y, kx, ky); then detection is carried out to a weak object in the image according to the generalized S transformation result S (x, y, kx, ky); by fixing the value of spatial frequency kx or ky, a four-dimensional S transformation result S (x, y, kx, ky) is reduced into three-dimensional data; and the combination of frequency component power of a vertical section image (y, ky) or (x, kx) corresponding to each value selected along x or y direction is compared with a threshold value so as to determine the positions of row and line of the weak object in the original image. The method carries out generalized S transformation to the original image; on basis of the generalized S transformation result, by dimension reduction and visualization processing, the information of the S transformation domain is utilized to detect the weak object, thus being capable of effectively overcoming the influences by obstruction factors such as noise, flooding wave, cloud, and the like, in the spatial domain.

The invention discloses a high-precision synchronous extraction generalized S transform time frequency analysis method. The method comprises the steps of firstly performing generalized S transform ona signal to obtain a generalized S transform value, thereby obtaining a time frequency spectrum of the generalized S transform; based on the generalized S transform value, calculating an instantaneousfrequency, obtaining a synchronous extraction operator (SEO) according to the instantaneous frequency, extracting the time frequency spectrum of the generalized S transform by utilizing the SEO, onlyreserving energy near a time frequency ridge line in a time frequency plane, removing all other divergent energy, and obtaining a synchronous extraction generalized S transform value; and finally converting the synchronous extraction generalized S transform value into a time frequency spectrum of synchronous extraction generalized S transform. The size of a window function can be flexibly adjusted according to the actual need; the method has higher time frequency decomposition precision and relatively high flexibility; and the time and frequency resolution of the signal is greatly increased.

The invention relates to a motor bearing fault diagnosis method based on generalized S transformation and WOA-SVM, and the method comprises the steps: inputting a motor bearing vibration signal, and obtaining two time-frequency matrixes after two times of different generalized S transformation; respectively obtaining a time domain cumulative characteristic curve with high time resolution and a frequency domain cumulative characteristic curve with high frequency resolution; obtaining a time domain feature and a frequency domain feature of the original signal; combining the time domain featuresand the frequency domain features to form a feature vector sample set, and dividing the feature vector sample set into training samples and test samples; inputting the training sample into a support vector machine optimized by a whale optimization algorithm WOA, and training a classifier; and inputting the test sample into the trained classifier WOA-SVM for testing, and outputting a fault diagnosis type. The method overcomes the defect that the Gaussian window function of S transformation cannot be adjusted along with the frequency and lacks flexibility, has better time-frequency analysis capability, and is more suitable for processing complex non-stationary and non-linear bearing vibration signals.

The present invention provides a stratum oil-gas possibility prediction method with a high time resolution. By utilizing improved generalized S transform, a Gauss window function with a time window width in directly proportional change with frequency f is employed, the time window is narrow at a low frequency band, the high time resolution is obtained, and seismic wave low frequency absorption attenuation gradient is calculated; at the same time, by using generalized S transform, a Gauss window function with a time window width in inversely proportional change with the frequency f is employed, the time window is narrow at a high frequency band, the high time resolution is obtained, high frequency absorption attenuation gradient is calculated, and the low frequency absorption attenuation gradient and the high frequency absorption attenuation gradient are utilized comprehensively. The method has better time resolution at a low frequency band, an influence of high frequency interference is avoided, at the same time, the method has better time resolution at a high frequency band, an influence of low frequency interference is avoided, oil and gas prediction time resolution is raised greatly, an algorithm is simple, and computational efficiency is high.

The invention provides a sparse feature extraction method for a fault signal of a rotating machine. The method includes the steps of constructing a fault overcomplete atom library according to a faultgeneration mechanism of an impact fault of the rotating machine; calculating a multi-resolution time-frequency spectrum of a to-be-analyzed signal through multi-resolution generalized S-transform resolution and normalization processing according to a mathematical model of a to-be-observed signal and the fault overcomplete atom library; calculating a maximum value of the normalized multi-resolution time-frequency spectrum, and calculating an atom set capable of best performing sparse representation on the to-be-observed signal by combination with an orthogonal matching tracking algorithm; andaccording to the fault sparse representation signal and fault feature frequency of a device and rotating speed information, determining a fault type, to achieve a rapid fault diagnosis of the mechanical device. The sparse feature extraction method has the advantages that the complexity of the atomic search and the search time are greatly reduced, the efficiency of sparse decomposition is improved,and the efficiency of fault diagnosis of impact faults such as crack, pitting or spalling is improved.

The invention relates to the technical field of geophysical prospecting for petroleum, and discloses a pre-stack Q-value inversion method based on generalized S-transform and a pre-stack Q-value inversion system thereof. The pre-stack Q-value inversion method comprises the steps that a pre-stack CMP gather of a seismic wave layer model is acquired; time frequency analysis s performed on the pre-stack CMP gather trace by trace by using generalized S-transform so that the amplitude spectrum of a target layer is obtained; the spectrum ratio slope of each seismic trace of the target layer is solved based on the amplitude spectrum of the target layer; a zero-offset ray average quality factor is solved according to the linear relation of the spectrum ratio slope and the offset; and the interlayer Q-value of the seismic wave layer model is estimated according to the zero-offset ray average quality factor. The accuracy of the inversion result can be greatly enhanced, reservoir prediction can be effectively guaranteed, and the method and the system can be applied to reservoir prediction, crack detection and inverse Q filtering of the field of oil and gas prospecting so that the resolution of seismic data can be enhanced.

The invention discloses a parameter optimization based time frequency analysis method for an improved generalized S-transform. The parameter optimization based time-frequency analysis method comprises the steps of firstly selecting the range of parameters in the improved generalized S-transform; for each group of parameters, calculating time frequency distribution of an input signal by using the improved generalized S-transform and carrying out energy normalization processing on the time frequency distribution; then calculating the corresponding time frequency aggregation degree of each group of parameters and employing one group of parameters corresponding to maximum values as optimized parameters; finally introducing the optimized parameters into the improved generalized S-transform to calculate the time frequency distribution of the input signal. According to the parameter optimization based time frequency analysis method for the improved generalized S-transform, a first-order function with frequency as an independent variable is introduced into a window function of the S-transform so that the form of the window function is flexible and adjustable, the parameters of the first-order function are optimized through the time frequency aggregation degree, the energy aggregation of the signal time frequency distribution can be improved, the estimated accuracy of signal instantaneous parameters is enhanced, the calculated amount is small, the parameter optimization based time frequency analysis method is suitable for analysis and processing of communication, radar, earthquake and biomedicine signals.

The invention relates to a generalized S transformation and SVM power quality disturbance efficient identification method comprising the following steps: collecting voltage data of a power quality disturbance signal, using a power quality monitor to collect the voltage data of the power quality disturbance signal, and transmitting the voltage data to an upper computer; gST generalized S transformation is carried out on the voltage data received by the upper computer, two groups of GST parameters are set, and a time amplitude envelope curve and a frequency amplitude envelope curve are respectively obtained; extracting a feature vector of the voltage data; and inputting the feature vector obtained in the previous step into a GWO-SVM classifier for training and testing, thereby completing identification of the power quality disturbance signal. The method has high identification precision in power quality disturbance identification, has strong anti-interference capability in power qualitydisturbance identification, and has good performance in processing a small sample disturbance identification problem.

The invention discloses a method for efficiently extracting an attenuation quality factor in a complex reservoir, comprising the following steps of S1, reading the seismic record data of a survey lineand determining a target stratum; S2, transforming, by using generalized S transform, each seismic record on the survey line from the time domain into the time-frequency domain to obtain a time-frequency map; S3, determining the attenuation estimated frequency band of the target stratum; S4, calculating the centroid frequency of a spectrogram in a selected frequency band at each time depth basedon the time-frequency map; S5, estimating the Q value sequence of the target stratum by an improved centroid frequency shift method; and S6, processing the Q value sequence by a stratum stripping method; and S7, drawing an output Q value profile map. The method utilizes the generalized S transform and the improved centroid frequency shift method to improve the accuracy and stability of the Q valueestimation.

The invention provides a hydrocarbon detecting method and a hydrocarbon detecting device based on wavelet energy attenuation features of generalized S transform, belonging to the field of seismic data processing of petroleum geophysical exploration. The method comprises that a time-frequency spectrum of each seismic channel is calculated according to the generalized S transform; a time-frequency spectrum of a wavelet of a corresponding sampling point is obtained from the time-frequency spectrum of each seismic channel via a spectrum simulation technology; a dominant frequency of each wavelet is found out from the time-frequency spectrum of the wavelet; between a zero frequency and the dominant frequency of each wavelet, an attenuation coefficient QL of the wavelet in a charging hole portion at an objective exploration layer is calculated; between the dominant frequency and a cutoff frequency of each wavelet, an attenuation coefficient QG of the wavelet in a gassing hole portion at the objective exploration layer is calculated; and gas hydrocarbon exploration results in the objective exploration layer are obtained according to the acquired energy attenuation feature of each wavelet. Due to the fact that change speed of attenuation coefficients at high/low frequency end of the wavelet is directly related to whether a reservoir contains gas, the method and device of the invention can obviously improve success rate of gas hydrocarbon exploration.

The invention discloses a small current grounding fault line selection method based on generalized S transformation transient energy. The method comprises the steps that: according to a 10kV line topological graph of a power distribution network, an actual overhead line-cable series-parallel line model and a fixed-value resistance fault model of the power distribution network are built on a PSCADplatform, and single-phase earth fault simulation experiments under various simulation working conditions are carried out; and then generalized S transformation is performed on the acquired transientzero-sequence current at the bus outlet in 1/4 power frequency period after each feeder line fails, generalized S transformation transient energy in a characteristic frequency band is calculated, andthe transient energy of each feeder line is compared so as to adaptively select a faulty line. The method is not influenced by the working conditions such as the fault position, the fault resistance,the line structure and the neutral point grounding mode, the fault line can be accurately selected; and the method has the advantages of being high in practicability, high in accuracy and wide in applicability.

The invention discloses a high-voltage circuit breaker fault diagnosis method based on GWO-KFCM. The method comprises the following steps of: 1, carrying out the band-pass filtering of a sound signal,and constructing a voiceprint feature through generalized S transformation; 2, carrying out variational mode decomposition on a vibration signal to obtain a permutation entropy; 3, combining the voiceprint features and the permutation entropy features to construct a feature vector set; and 4, pre-classifying the feature vector set through GWO-KFCM to obtain an optimal feature vector sub-set, inputting optimal feature vector sub-set data into a model SVM, obtaining a final diagnosis result through classification, extracting voiceprint and permutation entropy features respectively by utilizingthe joint complementary advantages of the sound and vibration signals, then performing clustering optimization by adopting GWO-KFCM to obtain an optimal clustering center, and finally, adopting an SVMfor fault diagnosis. The mechanical fault of the high-voltage circuit breaker can be quickly and effectively detected, and the reliability of fault diagnosis of the high-voltage circuit breaker is improved.

The invention discloses a fractional order synchronous extraction generalized S transform time-frequency decomposition and reconstruction method. The method comprises the following steps: inputting x(t), selecting different rotation angles alpha to carry out fractional Fourier transform spectral analysis on x (t); selecting an optimal rotation angle alpha opt to carry out fractional order generalized S transformation on the signal to obtain a fractional order generalized S transform value, and performing modeling; solving fractional order instantaneous frequency estimation on the basis of thefractional order generalized S transform value, obtaining a synchronous extraction operator according to the instantaneous frequency estimation, extracting a fractional order generalized S transformtime-frequency spectrum by using the synchronous extraction operator, retaining effective energy, and finally reconstructing a signal. According to the invention, the time-frequency characterization of the signal can be popularized to the time fractional order frequency characterization; time-frequency information details can be recognized by selecting the optimal rotation angle in a self-adaptivemode, on the basis, energy of a fractional order synchronous extraction generalized S transformation time-frequency spectrum is more gathered by adjusting window function parameters and conducting extraction operation, and therefore the time-frequency resolution of signals is greatly improved.

The invention relates to the field of petroleum and natural gas exploration and development, and especially relates to a high-resolution thin and interbedded reservoir gas containing performance detection method. The thin and interbedded reservoir gas containing performance detection method provided by the invention acquires a time-frequency spectrum of collected seismic signals through adopting generalized S transform. Compared with time-frequency conversion performed according to the traditional method, the method provided by the invention has higher time-frequency spectrum resolution and accuracy. Meanwhile, the thin and interbedded reservoir gas containing performance detection method combines a method of performing linear processing on the amplitude between a starting frequency fs andan ending frequency fe, thereby avoiding an operation of performing direct fitting by using data of a segment with the frequency f being greater than the starting frequency fs and less than the ending frequency fe to solve the slope, and greatly improving the resolution, which is reflected by the seismic signals, in the longitudinal direction of a target detection area.

The invention discloses a current signal filtering and denoising method based on generalized S transformation containing a variable factor. The method comprises the following steps: firstly, converting a discrete sampling sequence of a group of continuous time domain current signals into a two-dimensional time-frequency complex matrix through generalized S transformation containing the variable factor; secondly, performing threshold filtering method processing on the two-dimensional time-frequency matrix, performing processing by using a time-frequency filter based on time-frequency spectrum distribution, reserving effective signals in a time-frequency domain, and extracting effective current signal components; and finally, obtaining a noise-removed one-dimensional time domain current signal through generalized S inverse transformation. According to the method, the current pure signal under the noise background can be effectively extracted, so that the current signal can be analyzed and processed under various working condition environments.

The invention discloses a lightning trip type identification method, which comprises the following steps: extracting lightning trip transient voltage signal feature image data, inputting the feature image data into a deep residual neural network model, and outputting an identification result by the deep residual neural network model. The step of extracting the feature image data of the lightning trip transient voltage signal comprises the steps of performing multi-scale generalized S transformation on the extracted one-dimensional time domain voltage signal, drawing time-frequency distribution images of different-scale generalized S transformation, and taking the time-frequency distribution image data as the feature image data of the lightning trip transient voltage signal. According to the method, the one-dimensional time domain voltage signal is subjected to multi-scale generalized S transformation, so that the extraction range of lightning trip transient voltage signal feature image data is expanded, the problem that transient feature quantities are not sufficiently utilized by a one-dimensional signal analysis method in neural network discrimination is solved, and the accuracy of lightning trip type discrimination is improved.

The invention discloses an electrocardiogram identity recognition method. The method comprises the following steps: acquiring an ECG (Electrocardiogram) signal of a human body; preprocessing the collected ECG signal to obtain a clean short-period ECG signal; generalized S transformation is carried out on the short-period ECG signals, and a phase domain feature vector Y1, a time domain feature vector Y2 and a frequency domain feature vector Y3 are extracted; inputting the phase domain feature vector Y1, the time domain feature vector Y2 and the frequency domain feature vector Y3 into a sparsity-constrained nonlinear approximation model to obtain an optimal dictionary and a corresponding optimal sparse coefficient matrix, and performing lightweight processing on the optimal sparse coefficient matrix to obtain a sparse coefficient vector; and inputting the sparse coefficient vector into a trained deep neural network model based on a bidirectional long-short-term memory network for identity recognition. According to the invention, the multi-modal feature vector is extracted from the original ECG signal to serve as the input vector of the deep neural network, so that the recognition precision is improved, and the recognition rate is increased.

The invention relates to an action recognition method based on GST and VL-MOBPNN. The method comprises the steps: acquring sample data of human lower limb electromyographic signals; performing time-frequency generalized S transformation on an electric signal to obtain a time domain cumulative characteristic curve when the time resolution is relatively high and a frequency domain cumulative characteristic curve when the frequency resolution is relatively high; obtaining a feature vector of the signal; and inputting the feature vector into a learning rate variable quantity back propagation neural network for identification and classification to obtain a classification result. According to the invention, time-frequency generalized S transformation is adopted to analyze signals more meticulously, so that the problem that the window width of a Gaussian window is fixed in a traditional time-frequency analysis method is solved; Gaussian window adjustment parameters are introduced into time-frequency generalized S transformation, the inverse proportion change rate of the Gaussian window width along with the frequency can be flexibly adjusted according to the frequency distribution characteristics and the time-frequency analysis emphasis of the electromyographic signals in practical application, the method can better adapt to analysis and processing of specific signals, and therefore the signal processing capacity is improved.

The invention provides a seismic data frequency expanding processing method and device. The method comprises the following steps of: respectively carrying out empirical mode decomposition on logging data and seismic signals to obtain an intrinsic mode function corresponding to the logging data and an intrinsic mode function corresponding to the seismic signals; performing generalized S transformation on the intrinsic mode function corresponding to the logging data and the intrinsic mode function corresponding to the seismic signal to obtain a time-frequency spectrum corresponding to the logging data and a time-frequency spectrum corresponding to the seismic signals; fusing the two time-frequency spectrums to obtain a fused time-frequency spectrum; and performing generalized S transformation on the fused time-frequency spectrum, and performing superposition processing on the fused time-frequency spectrum after generalized S transformation and the residual components to obtain seismic data after frequency expansion. According to the method, the high-frequency components in the logging data are fused into the seismic data, so that the seismic data frequency band is widened, the main frequency of the seismic data is improved, the seismic data resolution can be improved, and the capacity of identifying a thin reservoir by the seismic data is improved.