92 results about "Morlet wavelet" patented technology

The invention discloses a fault diagnosis method for a rolling bearing, and belongs to the technical field of fault diagnosis and signal processing analysis. High-frequency sampling and preprocessing of vibration signals are firstly performed on the rolling bearing with faults, the preprocessed signals are sequentially filtered by the aid of a Morlet wavelet filter, spectral kurtosis and unit spectral kurtosis of the filtered signals are calculated, and a filter parameter corresponding to the maximum value of the unit spectral kurtosis is selected by comparison, namely, a global optimization filter parameter is selected. According to the fault diagnosis and detection method, an operator can extract the optimal filter parameter in the diagnosis process without a lot of detection experience and numerous historical data, the method is wider in application range, errors caused by human errors are greatly decreased, the extracted optimal filter parameter can be more accurate, a diagnosis result is more correct, fault diagnosis and detection automation is more facilitated, more time is saved, and efficiency is higher.

The invention belongs to the mechanical fault diagnosis field, especially relates to application of Morlet wavelet transformation and convolutional neural network, to be specific, provides a bearing fault diagnosis method based on Morlet wavelet transformation and convolutional neural network. Morlet wavelet transformation coefficient matrixes of vibration signals can be used as the input of the convolutional neural network after the uniformization. In the training phase of the convolutional neural network, the learning algorithm provided with the labels having the monitoring function can be adopted, and the minimization adaption function rule can be adapted, and then the weight and the offset of every layer can be adjusted by using the gradient descent with the momentum term. The trained convolutional neural network is used for the classification of the bearing faults, and the diagnosis of the bearing faults can be realized by explaining the classification result. The Morlet wavelet transformation and the convolutional neural network can be combined together for the diagnosis of the bearing faults, and the processing of the original classification data is simpler than that of the prior art, and after the test, the diagnosis identification rate of the self-built sample database can reach more than 80%.

The invention relates to the technical field of petroleum exploration, in particular to a time-frequency decomposition earthquake-fluid recognition method which includes establishing a time-frequency atom dictionary D according to Morlet wavelet function m(t)=exp[-betaXf2(t-tau)2]exp[i(2pif(t-tau)+phi)], and acquiring an initial matching atom of the Morlet wavelet function through calculation with a seismic trace and complex seismic trace method; performing matching decomposition on the seismic trace, performing iterative optimization with constraints of the time-frequency atom dictionary D in the neighborhood of the initial matching atom to acquire an optimal matching atom, stopping matching decomposition when preset conditions are achieved, and representing the initial seismic trace as a series of linear combinations of Morlet wavelet atoms; transforming the optimal matching Morlet atom into the time-frequency domain so as to acquire a time-frequency spectrum distribution of the initial seismic trace; extracting directly properties of earthquake fluid activity on a target stratum section on the time-frequency spectrum of earthquake materials; and predicting distribution range and space distribution of gas deposit according to the properties of the fluid activity. By the method, the distribution range and the space distribution of the gas deposit can be accurately predicted, so that a technical support is provided for favorable target optimization of natural gas exploration.

A windmill blade crack detection method is characterized in that the method comprises the following steps: 1, installing an acoustic emission sensor on a windmill blade, transmitting a received acoustic emission signal to an acoustic emission acquisition system, and determining the sampling frequency, the sampling length and the filtering frequency of the signal; 2, optimizing a bandwidth parameter of an Morlet wavelet basis function based on Shannon wavelet entropy to obtain the Morlet wavelet basis function matched with the characteristics of acoustic emission signals of an expansion crack and a generated crack, and calculating the redistribution scale spectrum of the acoustic emission signals in order to determine the crack state; and 3, determining the expansion state of the crack fault according to the time frequency characteristic parameters of the extracted crack acoustic emission signals. The method can rapidly and accurately detect the dynamic expansion state of the blade cracks, guarantees the safety and the high efficiency of the wind mill blade, prolongs the service life of the wind mill blade, and reduces the maintenance cost.

A conjoint analysis method for electroencephalograph and electromyography signals based on autonomous movement and imagination movement comprises the steps of performing system setup, and using a LabVIEW 8.6 to generate square wave pulse signals; respectively collecting electroencephalograph signals and electromyography signals including electroencephalograph signals and electromyography signals in autonomous movement modalities and in imagination movement modalities; performing noise removal pretreatment on collected original data; performing electroencephalograph and electromyography time-domain signal analysis in the autonomous movement and imagination movement modalities on electroencephalograph and electromyography signal time-domain pictures which are performed with noise removal pretreatment in the autonomous movement and imagination movement modalities; performing time-frequency signal analysis on electroencephalograph and electromyography signals performed with noise removal pretreatment and in the autonomous movement and imagination movement modalities based on Morlet wavelet transformation; and performing partial directional coherence analysis, and in particular adopting granger causality to perform the partial directional coherence analysis. The conjoint analysis method provides new evaluation parameters for monitoring recovery auxiliary equipment and assessing organism movement level.

The invention discloses a battery health state estimation method based on on-line electrochemical impedance spectroscopy measurement. According to the battery health state estimation method based on on-line electrochemical impedance spectroscopy measurement, a Morlet wavelet is used for signal processing of voltage and current data of a battery collected under the actual use condition of a battery, an EIS of the battery is obtained, and then an SOH of the battery is characterized by the EIS; compared with data-driven methods, a large number of experiments do not need to be conducted, and universality is high; and compared with existing methods based on electrochemical model parameters or equivalent circuit model parameters, calculation is easy, and the accuracy degree is high. According tothe method of using the Morlet wavelet to transform the collected voltage and current data of the battery under the actual use condition to obtain the EIS of the battery has the advantages of simplicity, low cost, high efficiency and on-line analysis compared with a method of measuring the EIS by applying excitation.

The invention discloses a transformer winding state diagnosis method. The method comprises the following steps: (1) a vibration signal of each measurement point of a transformer winding is acquired; (2) fourier transform is performed on the vibration signal to obtain a vibration frequency response curve of the transformer winding; (3) fourier inversion is performed on the vibration frequency response curve to obtain a free vibration signal of the transformer winding; (4) wavelet transform is performed on the free vibration signal with Morlet wavelets; (5) a wavelet transform time-frequency diagram is formed; (6) wavelet ridges of the wavelet transform time-frequency diagram are extracted, wherein the vertical coordinate of each wavelet ridge is the inherent frequency of the transformer winding; (7) and the state of the transformer winding is judged according to the change of the inherent frequency.

The invention discloses a fault diagnosis method of a variable-speed bearing, which comprises the following steps of: sampling the vibration signals of the bearing at equal time intervals through an acceleration sensor by a data acquisition module controlled by a fault diagnosis module to obtain a vibration signal sequence x(n); carrying out wavelet transformation on the acquired vibration signal sequence x(n) by adopting a plurality of Morlet wavelets as the wavelet basis functions of the wavelet transformation to obtain wavelet coefficient wt(m,n); carrying out modular operation on the wavelet coefficient wt(m,n) to acquire the envelope ewt(m,n)=||wt(m,n)|| of the wavelet coefficient wt(m,n); transforming the wavelet envelope coefficient at each size from an equal time interval sampling result to an equal angle sampling result; carrying out Fourier transformation on the wavelet envelope sequences ewt(m,t) at various sizes, which are subjected to equal angle sampling, to obtain the frequency spectra eswt (m,f)=FFT(ewt(m,t)) of the wavelet envelope sequences ewt(m,t); and making eswt(m,f) into a three-dimensional image.

A method for analyzing relativity between electroencephalograph and myoelectricity based on autonomous and stimulation movement modalities comprises the steps of performing system setup, and using a LabVIEW 8.6 to generate synchronizing pulse signals; respectively collecting electroencephalograph signals and electromyography signals including electroencephalograph signals and electromyography signals in autonomous movement modalities and in stimulation movement modalities; analyzing electroencephalograph and electromyography time-domain signals in the autonomous movement and stimulation movement modalities according to time domain pictures of electroencephalograph and electromyography signals of a subject in the autonomous movement and stimulation movement modalities; removing noise of the electromyography signals in the stimulation modality; performing time-frequency analysis on electroencephalograph signals based on Morlet wavelet transformation; and performing coherence analysis. The method can obtain activating or restraining information of electroencephalograph in different time frequency in initiative and passive states to be used for guiding and feeding back recovery indexes of physical disability patients of apoplexy patients and the like, thereby enabling recovery to be a quantitative process instead of a qualitative definition.

The embodiment of the invention discloses a method for filtering the spectral kurtosis of a signal and a relevant device, and is used for filtering the signal with high background noise and extracting a transient feature of the signal. The method comprises the following steps of: acquiring an input signal; building a Morlet wavelet filter group which takes the signal feature frequency of the input signal as a center frequency and has different bandwidths; filtering the input signal according to the Morlet wavelet filter group, and calculating a signal square envelop; calculating the spectral kurtosis according to the signal square envelop, and obtaining a spectral kurtosis damp ratio matrix; adaptively acquiring a Morlet wavelet filter with the optimal bandwidth according to the spectral kurtosis damp ratio matrix; and filtering the input signal by using the Morlet wavelet filter with the optimal bandwidth, and extracting the transient feature of the input signal.

The invention relates to a method for improving the resolution ratio of seismic data and enhancing energy of valid weak signals. The method needs to process stacked or migrated seismic data channel by channel, and carries out the following steps on single-channel seismic data: 1) Hilbert transformation is performed on the single-channel seismic data, the envelope, the time shift quantity and the instantaneous attribute of the single-channel seismic data are obtained, and combined with an average wavelet extracted by well-seismic calibration, a Morlet wavelet library is built; 2) based on the wavelet library built in the step 1), a matching pursuit algorithm is used to obtain valid wavelets through decomposition which form the single-channel seismic data; 3) a spectrum whitening algorithm is improved, and self-adaptive frequency extension and energy compensation processing are performed on the valid wavelets obtained through decomposition; and 4) valid wavelets after the processing of the step 3) are superposed, thereby obtaining single-channel seismic data with an improved resolution ratio and enhanced energy of valid weak signals. The method integrates logging and seismic data, and combines spectrum whitening algorithm improvement with the matching pursuit algorithm, thereby achieving effects of broadening a data frequency band and enhancing energy of the valid weak signals.

The invention relates to an extreme learning machine classifying method based on waveform addition Cuckoo optimization. The extreme learning machine classifying method mainly comprises the steps that (I) a training sample matrix is established; (II) M initial parasitic nests are generated on each hidden node; (II) the classifying accuracy of a waveform addition extreme learning machine classifying model is solved; (IV) training samples are randomly and equally divided into parts (please see the number of the parts in the specification), and the classifying accuracy output value of the extreme learning machine classifying model verified in a cross mode is solved; (V) an inverse hyperbolic sine function and a Morlet wavelet function are superposed to serve as an excitation function of the extreme learning machine, the waveform addition extreme learning machine classifying model is structured, and the current generation classifying accuracy of a Cuckoo algorithm is obtained; (VI) a next generation result of the Cuckoo algorithm is solved, and parasitic nests are newly established with the probability Pa; (VII) repeated iteration is conducted, whether the iteration is ended is judged, an optimal extreme learning machine classifying model is established if ending conditions are met, and the optical extreme learning machine classifying model is used for classifying unknown samples. The extreme learning machine classifying method is low in calculation complexity, high in efficiency, stable in classifying performance, high in accuracy and high in global optimization and generalization performance.

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.

The invention discloses a flush type bearing failure intelligent diagnosing device based on an ARM (advanced RISC machine) and a DSP (digital signal processor). The device comprises a signal input module connected with a vibration signal of a detected bearing, wherein the signal input module is connected with a core operation module, a serial communication module and a control module in sequence. A structure of DSP plus ARM is used in the invention, and an ARM sub-system and a DSP sub-system are communicated through an RS232 (recommend standard 232) serial bus; and under the system structure, real-time sampling and calculation as well as the management and control of a system can be executed in parallel, and the two sub-systems are synchronized on task execution by virtue of communication between the two sub-systems. The dual-CPU (central processing unit) structure-based system plays a critical role in protecting the real time property, and a multi-scale envelope spectrum analysis algorithm based on Morlet wavelet transformation is adopted in the core algorithm, and is realized in flush-type hardware for the first time, so that not only can the accuracy of bearing failure diagnosis be improved, but also the portability is realized.

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.

The invention provides an LSSVM (Least Square Support Vector Machine) pulsation wind speed prediction method based on a Morlet wavelet kernel. The prediction method comprises the following steps: utilizing an ARMA (Auto-Regressive and Moving Average) model to simulate and generate a vertical spatial point pulsation wind speed sample, dividing the pulsation wind speed sample of each spatial point into two parts including a training set and a test set, and carrying out normalization processing on the two parts; establishing an LSSVM model of the Morlet wavelet kernel; utilizing a Morlet wavelet kernel model subjected to PSO (Particle Swarm Optimization) to transform a pulsation wind speed training sample into a kernel function matrix, and mapping the kernel function matrix into a high-dimensional characteristic space; obtaining a nonlinear model of the pulsation wind speed training sample, and utilizing the model to predict the pulsation wind speed training sample; and comparing the wind sped of the test sample with a predicated pulsation wind speed, and calculating an average error, a root-mean-square error and a relevant coefficient of predicted wind speed and practical wind speed. The accuracy of pulsation wind speed prediction is guaranteed, and new wavelet kernel function selection with high precision and stability is provided.

The invention provides a frequency division diffusion filter fault strengthening method based on optimized Morlet wavelet. The method comprises the following steps: converting post-stack seismic datainto an MATLAB identifiable data format; carrying out spectral analysis on seismic data and extracting the wavelet; improving a Morlet wavelet generating function and constructing a Morlet wavelet function composition; carrying out wavelet frequency division processing by utilizing the optimized Morlet wavelet; calculating a structural tensor of the post-stack seismic data and further constructingnew fault factors based on the structural tensor; under the protection of the structural tensor, carrying out anisotropic diffusion filter on frequency division seismic data; and carrying out waveletinverse transformation on a frequency division filter result and reconstructing to obtain improved signal-to-noise ratio and resolution ratio and protect the seismic data of fault information. The method has the advantages that the resolution ratio of the seismic data is improved while denoising and edge protecting are realized, so that the low-order fault recognition ability of the seismic datais improved.

The invention relates to a structural modal parameter identification method used for identification of modal frequency, damping and vibration mode of civil and space structures. The method comprises the following steps: carrying out experience mode decomposition on a noisy vibration testing signal of a structure, and carrying out power spectrum analysis on an obtained intrinsic mode function component after decomposition; according to a frequency structure of each intrinsic mode function component, selecting an intrinsic mode function component containing structural modal information, and carrying out linear superposition to obtain a reconstruction signal; utilizing the reconstruction signal to carry out Morlet wavelet transformation, carrying out least square linear fitting on instantaneous amplitude logarithm and phase of a wavelet transformation coefficient corresponding to each modal, and calculating modal frequency, modal damping and modal vibration mode of the structure.

The invention discloses a sparse representation method for extracting weld characteristic guided wave defect echo signal characteristics. The sparse representation method for extracting the weld characteristic guided wave defect echo signal characteristics comprises the steps that sparsity of echo signals are used to provide a novel defect feature extraction method; a parameter-adjustable echo signal sparse representation model is established; in the model realization process, an Morlet wavelet atom most similar to a drive signal is adopted to be the basis to construct an overcomplete atomic dictionary, and then a base tracking denoising model is solved through a split augmented Lagrange contraction algorithm; according to wave crest arrival time in obtained reconstructed signals represented by sparse representation, accurate positioning of deflect positions is achieved; and the effectiveness of the method is verified through simulated and actual weld defect signals.

The invention relates to a continuous wavelet transform object tracking method based on space-time processing block, comprising the following four steps: step 1, obtaining a space-time three-dimensional processing block Bt and a space-time continuous wavelet function; step 2, adopting Morlet wavelet as a wavelet base function, and obtaining a space-time three-dimensional wavelet function for performing space-time continuous wavelet transform on the processing block Bt through function transform; step 3, obtaining the movement parameter of the moving object in the (Zf+1)-th frame image window of the processing block Bt according to the corresponding relationship of the (Zf+1)-th frame image window of the processing block Bt and the (t-1)-the frame image of the image sequence S; step 4, obtaining the movement parameter of the moving object in the t-th frame image of the image sequence S according to the corresponding relationship of the (Zf+1)-th frame image window of the processing block Bt and the (t-1)-th frame image of the image sequence S, and the corresponding relationship of the (Zf+2)-th frame image window of the processing block Bt and the t-th frame image of the image sequence S. The method is scientific and reasonable in design, simple in program, and has good practical value and broad application prospect in the technical field of machine view and pattern recognition.

The invention relates to a fracture time-frequency representation based matching pursuit frequency division method, which comprises the steps of S1, calculating a complex seismic trace by Hilbert transform on the basis of a fracture network medium model, S2, calculating the instantaneous envelope, the instantaneous phase and the instantaneous frequency of the complex seismic trace, S3, finding an envelope maximum value and a corresponding time position, and acquiring an estimated value of Morlet wavelet amplitude, S4, calculating the instantaneous phase and the instantaneous frequency at the corresponding time, and acquiring estimated values of a Morlet wavelet phase angle and the main frequency of fracture medium optimization matching, S5, subtracting a real part of the determined Morlet wavelet from the original seismic trace, and S6, repeating the steps S1-S5 until the left seismic trace amplitude is less than a given threshold. The beneficial effect lies in that the fracture time-frequency representation based matching pursuit frequency division method is good in adaptivity, high in noise resistance, and good in time and frequency resolution controllability.

Embodiments of the present invention apply wavelets to radio frequency (RF) signals to extract specific characteristics (e.g., jitter, phase variations, frequency variations) so that their timing, phase, and frequency components can be characterized. In one embodiment of the present invention, a Haar wavelet is used to extract timing characteristics. In another embodiment, a Morlet wavelet is used to extract phase characteristics. In still another embodiment, a Morlet wavelet is used to extract frequency characteristics.

The invention discloses a voice extraction method under high background noise. At first, original voice signals containing the high background noise are discretely sampled in advance and quantized to obtain data frames, a wavelet neural network based on a Morlet wavelet function is constructed for the data frames, a particle swarm fitness function is constructed for parameters of the wavelet neural network, then the optimal parameter is obtained through a particle swarm algorithm, the data frames are input to the wavelet neural network to be filtered, and therefore the noise is removed, and voice signals are extracted. The parameters of the wavelet neural network are obtained by means of the particle swarm algorithm. Therefore, fitness for different environmental noise characteristics can be improved through the method.

The invention discloses a rolling bearing residual life prediction method considering model and data uncertainty. The method comprises the steps of collecting a rolling bearing full life cycle vibration acceleration signal; extracting a morlet wavelet transform time-frequency diagram of the vibration acceleration signal; constructing health factor data by utilizing a multi-scale deep convolutionalnetwork, and meanwhile, obtaining a model uncertainty quantitative analysis result by adopting a variational inference method; performing regression prediction analysis on the health factor data by utilizing an improved relevance vector machine, predicting the residual life, and quantitatively analyzing the data uncertainty at the same time; and comprehensively considering model uncertainty and data uncertainty quantitative analysis results to obtain a prediction result confidence interval. Improvements related to the prior art are as follows: a polynomial regression prediction model is fusedinto a relevance vector machine, so that the residual life prediction precision is improved; uncertain factors in residual life prediction are comprehensively considered, model uncertainty and data uncertainty are quantitatively analyzed, and the reliability of a prediction result confidence interval is improved.

The invention belongs to the technical field of coal mining machine rocker arm mechanical transmission system fault diagnosis, and particularly relates to a coal mining machine rocker arm mechanical transmission system fault accurate positioning method. The method comprises the steps of 1, collecting rocker arm vibration signals in normal and fault states, and performing noise reduction processingon the rocker arm vibration signals through a wavelet transformation method; 2, performing FFT conversion to obtain a spectrogram of the vibration signal; 3, performing comparative analysis on the normal rocker arm spectrogram and the fault rocker arm spectrogram to obtain vibration characteristic frequency of a fault part, and performing preliminary positioning on the fault part; 4, comparing and analyzing the continuous complex Morlet wavelet envelope demodulation spectra of the normal rocker arm and the faulty rocker arm to obtain the rotation frequency of the faulty part; and 5, accurately positioning a faulty part by combining adaptive continuous complex Morlet wavelet envelope demodulation analysis with FFT. The method has important practical significance for guaranteeing safe operation of the coal mining machine, changing preventive periodic maintenance into predictive maintenance, safely and efficiently producing a coal mine, improving the maintenance efficiency and reducing the maintenance cost.