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640 results about "Wavelet packet decomposition" patented technology
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Originally known as Optimal Subband Tree Structuring (SB-TS) also called Wavelet Packet Decomposition (WPD) (sometimes known as just Wavelet Packets or Subband Tree) is a wavelet transform where the discrete-time (sampled) signal is passed through more filters than the discrete wavelet transform (DWT).
Low current neutral grounding system fault route selecting method by wavelet package decompose and correlation analysis
InactiveCN101162838ARealize correct line selectionStrong anti-arc grounding abilityEmergency protective circuit arrangementsFault locationElectric power systemDecomposition
The present invention relates to a small current grounding system fault line selecting method by wavelet package decomposition and relevant analysis, belonging to the power system relay protection technical field. The method comprises: starting the fault line selecting device and recording wave to acquire transient zero-order current on each of the feeder lines when bus zero-order voltage transient value exceeds limit; calculating energy on each of frequency bands of the transient zero-order current after wavelet package decomposition, summing the energy of transient zero-order current on all the lines according to frequency bands and selecting the frequency bands with the maximum and secondary value of energy and values as the characteristic frequency bands, applying relevant analysis method to line transient zero-order current on the selected characteristic frequency bands in order; and finally determining fault point integrating the relevant analysis results of two frequency bands. The method uses wavelet package decomposition and relevant analysis, thereby effectively using information (amplitude and phase) contained in fault transient process and reflecting differences between fault line and non-fault line to maximum degree. Principle analysis and simulations demonstrate that the method has precise and reliable line selection.
Owner:KUNMING UNIV OF SCI & TECH
Fusion diagnosing method of centrifugal pump vibration accidents and vibration signals sampling device
InactiveCN1920511AImplementing a normal status signalComprehensive signal acquisitionMachine part testingPump testingEngineeringNormal state
The invention relates to an eccentric pump vibration accidence fusion diagnose method and relative vibration signal collector, wherein said invention is characterized in that: it uses the eccentric pump vibration signal collector to collect the normal state, the quantity imbalance, asymmetry rotate and loose base of eccentric pump; uses wavelet decomposition and reconstruction to extract the character of vibration signal; and inputs the character vectors into sub fuzzy neural networks I and II; to be treated and replace the relation factor matched with sensor signal function; the whole fuzzy neural network comprises data fuzzy layer, input layer, hidden layer and output layer; uses D-S theory to obtain the fused signal function distribution, realize the fusion diagnose on normal state, quantity imbalance, asymmetry rotate and loose base. The invention has simple structure and high effect.
Owner:NORTHEAST DIANLI UNIVERSITY
Computer program and method for detecting and predicting valve failure in a reciprocating compressor
Embodiments of the present invention provide a method implemented by a computer program for detecting and identifying valve failure in a reciprocating compressor and further for predicting valve failure in the compressor. Embodiments of the present invention detect and predict the valve failure using wavelet analysis, logistic regression, and neural networks. A pressure signal from the valve of the reciprocating compressor presents a non-stationary waveform from which features can be extracted using wavelet packet decomposition. The extracted features, along with temperature data for the valve, are used to train a logistic regression model to classify defective and normal operation of the valve. The wavelet features extracted from the pressure signal are also used to train a neural network model to predict to predict the future trend of the pressure signal of the system, which is used as an indicator for performance assessment and for root cause detection of the compressor valve failures.
Owner:UNIVERSITY OF MISSOURI
Intelligent fault diagnosis method suitable for microgrid
The invention discloses an intelligent fault diagnosis method suitable for a microgrid. The method comprises the following steps: monitoring the operation state of the microgrid through acquiring voltage current signals on a bus of the microgrid, determining whether an internal fault occurs in the microgrid, and accordingly, determining the starting mode of the diagnosis of the microgrid fault; according to the set condition of a fault diagnosis mode, acquiring the voltage current signals on the bus of the microgrid and carrying out preprocessing; by use of a multi-wavelet package decomposition technology, extracting fault signal transient components of different frequencies; calculating wavelet singular entropy as a characteristic vector of an RBF nerve network fault diagnosis mode of the microgrid; and inputting the characteristic vector into the diagnosis mode, outputting fault state information of the microgrid. According to the invention, a microgrid external fault diagnosis function and an internal short circuit fault diagnosis function are simultaneously provided, and a basis is provided for operation control and correlation protection of the microgrid.
Owner:STATE GRID CORP OF CHINA +1
Rolling bearing fault diagnosis method based on time-frequency domain multidimensional vibration feature fusion
ActiveCN104655423AIncrease computational time complexityImprove diagnostic accuracyMachine bearings testingEngineeringEuclidean vector
The invention provides a rolling bearing fault diagnosis algorithm based on time-frequency domain multidimensional fault feature fusion. Aiming at the respective features of vibration signals of a rolling bearing in a normal state, a roller fault state, an inner ring fault state and an outer ring fault state in a time-frequency domain, through extraction of time domain and frequency domain features, redundancy removal and re-fusion, fault features are described in an optimal way to obtain an intelligent judgment result. First, wavelet de-noising is performed on extracted original rolling bearing vibration data; then, time domain feature vectors are extracted to form a time domain feature matrix, and coefficient energy moments after wavelet packet decomposition and reconstruction are extracted to form a frequency domain feature matrix; and the time and frequency domain matrixes are further fused to obtain a time-frequency domain multidimensional fault feature matrix. Redundancy of the multidimensional feature matrix is eliminated to obtain a new multidimensional feature matrix. Then, information of multidimensional features is fused with a weighted feature index distance, and a state judgment result of the rolling bearing is obtained through the feature index distance obtained through fusion.
Owner:BEIJING JIAOTONG UNIV +1
BP network based pipeline security identifying method for optical fiber pipeline leakage monitoring device
InactiveCN101183899AImprove securityLow implementation costNon-electrical signal transmission systemsElectromagnetic transmissionFiberData acquisition
The invention relates to an identification method for the pipeline safety based on BP network and used in a monitoring device for the fiber pipeline leakage, belonging to the pipeline monitoring technique. The method comprises the following steps: the voltage signal is acquired after the photoelectric conversion of two channels of interference light in the monitoring device for the interference typed distributed optical fiber pipeline by utilizing a synchronous data acquisition card with at least double channels; the eigenvector of the detection signal obtained by calculation is decomposed by the wavelet; a plurality of acquisition samples are selected for each intrusive event in order to train and test a BP neural network; after the model training is completed, the system can acquire the real-time testing signal, extract signal characteristics, import into the well trained model for the online identification of the types of the abnormal events occurring along the pipeline and locate the abnormal events. The invention has the advantages of simple and convenient method, small occupied resource of the system, good real-time quality, which can be realized by a plurality of forms.
Owner:TIANJIN UNIV
Rolling bearing fault detection method based on vibration detection
InactiveCN103076177AEliminate distractionsRich in detailsMachine bearings testingDiagnosis methodsFrequency characteristic
The invention relates to a fault diagnosis method, in particular to a rolling bearing fault diagnosis method based on the vibration detection. The method comprises the following steps of firstly decomposing the rolling bearing data collected by an acceleration sensor into three layers of wavelet packets, solving the energy of a third layer of wavelet packet coefficient rebuilding signals, selecting a frequency band with centralized energy to rebuild approximate evaluation of an original signal according to the variation of energy values of each frequency bands of the third layer; and utilizing a cepstrum to further analyze the rebuilt signal, and comparing the rebuilt signal with a theoretically-computed fault characteristic frequency and an edge frequency characteristic. By combining multiple resolutions of the wavelet packet and the cepstrum, the periodic component on a power spectrum, a separated-side frequency-band signal and the characteristics which are slightly subjected to the transmission route can be well detected. Meanwhile, the method is strong in manipulability and practicability.
Owner:KUNMING UNIV OF SCI & TECH
Method and apparatus for improving signal to noise ratio using wavelet decomposition and frequency thresholding
InactiveUS6741739B1Good removal effectImage degradationImage enhancementImage analysisSignal-to-noise ratio (imaging)Wavelet decomposition
A method and apparatus for improving the signal to noise ratio of an information carrying signal wherein a wavelet transform up to a predetermined level is computed, a frequency thresholded signal which is indicative of noise is derived from the wavelet transform, and the frequency thresholded signal is subtracted from the information carrying signal.
Owner:EXCELIS INC
Single-phase earth fault line selection method based on transient state high-frequency component correlation analysis
ActiveCN108663599AOvercome the problem of not being able to select the correct lineImprove reliabilityFault location by conductor typesCapacitanceEngineering
The invention dislcoses a single-phase earth fault line selection method based on transient state high-frequency component correlation analysis. The method comprises the steps that whether or not a single-phase earth fault happens is judged according to the magnitude of a busbar zero-mode voltage instantaneous value, db10 wavelets are adopted for performing five-layer wavelet packet decompositionon zero-mode current of two work frequency periods after each feeder line fault, after the minimum frequency band is removed, node wavelet packet deposition coeffiences are reconstructed and summeredto obtain a fault transient high-frequency capacitance-current component, and a correlation coeefient matrix M between every two feeder lines is obtained; Si is set as a correlation accumulation coefficient for the ith feeder line fault transient high-freuqency capacitance-current component, when rho ij is larger than rho set, Si is equal to 1, 1 is subtracted from Si, and a correlation accumulation coefficient matrix S can be obtained; if Si is larger than 0 constantly, the busbar breaks down; if Si of only one feeder line is smaller than 0, the circuit is a fault circuit; if Si is smaller than 0 constantly, the circuit with the maximum high-frequency-band wavelet packet energy sum is determined as the fault circuit. According to RTDS simulation experimental verification, the method has the good accuracy, adaptability and the like.
Owner:TAIYUAN UNIV OF TECH
Capacity configuration method of hybrid energy storage system for stabilizing wind power fluctuation
ActiveCN103580041AReduce energy storage capacityGuarantee grid connection securityEnergy storageAc network load balancingSystem capacityGrid connection
The invention provides a capacity configuration method of a hybrid energy storage system for stabilizing wind power fluctuation. The hybrid energy storage system comprises a power type energy storage system and an energy type energy storage system. The method includes the steps that (1) the grid connection active power of wind power and the active power P (t) of the hybrid energy storage system are acquired, and a wavelet packet decomposition method is utilized to acquire power type energy storage power and energy type energy storage power respectively; (2) the hybrid energy storage system at different rated power P and under different capacities is configured, and the stabilizing effect of the hybrid energy storage system is analyzed; (3) according to the stabilizing effect and an improved particle swarm algorithm, the capacity of the hybrid energy storage system is configured. Compared with the prior art, according to the capacity configuration method of the hybrid energy storage system for stabilizing wind power fluctuation, a control policy is built according to the current electric quantity conditions, power magnitude required to be exerted and the rated power of the power type energy storage system and the energy type energy storage system and the improved particle swarm algorithm, and wind power fluctuation is stabilized and effectively controlled.
Owner:STATE GRID CORP OF CHINA +2
Twitter identification method based on self-adaption energy detection under complex environment
InactiveCN103489446AImprove efficiencyEasy to identifySpeech analysisPattern recognitionNoise power spectrum
The invention relates to a twitter identification method based on self-adaption energy detection under a complex environment. The method is characterized by comprising the following steps of firstly, dividing twitter into three frequency bands according to the frequency distribution condition of the twitter; secondly, detecting twitter acoustical signals containing noise through a self-adaption energy detection method based on noise power spectrum estimation and the foreground sound existence probability, and screening out the useful twitter signals; thirdly, extracting Mel-scale WPSCC features from the useful twitter signals according to distribution of the Mel scales; fourthly, conducting modeling, classification and identification on the extracted Mel-scale WPSCC features and extracted Mel-scale MFCC features through an SVM classifier. The WPSCC features extracted through the method have a better anti-noise function and are better in identification performance after undergoing self-adaption energy detection, the method is more suitable for twitter identification under the complex environment and has a good classification and identification effect on the twitter sound containing noise under the complex environment.
Owner:FUZHOU UNIV
Health assessment and fault diagnosis method for rotating machinery based on fisher discriminant analysis and mahalanobis distance
ActiveCN102944435ASolve the hot issues of comprehensive health managementRealize intelligent maintenanceStructural/machines measurementSpecial data processing applicationsDiagnosis methodsMahalanobis distance
The invention discloses a health assessment and fault diagnosis method for rotating machinery based on fisher discriminant analysis and a mahalanobis distance, which belongs to the technical field of condition-based maintenance of the rotating machinery. The method comprises the steps of extracting an energy eigenvector based on wavelet packet decomposition, constructing a discriminant analysis function, conducting health status assessment, conducting fault detection on the rotating machinery, and finally conducting fault diagnosis on the rotating machinery. The method constructs a comprehensive frame integrating the status assessment, the fault detection and the fault diagnosis, solves the hotspot problem in comprehensive health management of the rotating machinery at present, achieves intelligent maintenance of the rotating machinery, can establish an assessment and diagnosis model without full life status monitoring data of the rotating machinery, reduces the dependence on historical data, and is very high in engineering applicability.
Owner:北京恒兴易康科技有限公司
Distribution network cable-wire mixed line failure route selection method by utilizing relative energy
ActiveCN101404408AIncreased sensitivityImprove reliabilityEmergency protective circuit arrangementsFault locationCapacitanceRelative energy
The invention relates to a failure line selection self-adapting method for a mixed line of a distribution network line-cable by utilizing the relative energy of a characteristic frequency band. The method is as follows: when the instant value of the zero sequence value of a bus is out of limit, a failure line section device instantly starts and records waves; a morphological filter is used for carrying out filter pre-treatment on the transient zero-sequence currents of temporary state of each line; a wavelet packet is applied for decomposing, the characteristic frequency bands of the transient zero-sequence currents of temporary state of each line are confirmed, the component of a stable state and the information of other frequency bands are eliminated and the line selection margin is improved according to the view of energy maximizing; the sizes of the relative energies in the front 1 / 4 period and the last 1 / 4 period in the back period of the failure under the characteristic frequency bands are compared according to the principle that the capacitance and current of the failure line under the temporary state attenuates fast in the characteristic frequency bands, thus forming the criterion for failure line selection. Principle analysis and imitation show that the relative energy of the first 1 / 4 period and the last 1 / 4 period in the back period of the failure is utilized for realizing the line selection, thus amplifying the temporary characteristics of the failure, avoiding the effect of an intermittent corner on the accuracy of the line section caused by the saturation of CT. The method is accurate and reliable for line selection.
Owner:KUNMING UNIV OF SCI & TECH
Method and device for diagnosing mechanical characteristic failures of high-voltage circuit-breaker
ActiveCN103616635ASmall amount of calculationMachine part testingCircuit interrupters testingCommunication unitSpectral entropy
The invention belongs to the technical field of circuit-breaker failure diagnosis, and particularly relates to a method and device for diagnosing mechanical characteristic failures of a high-voltage circuit-breaker. The device comprises the circuit-breaker and further comprises a vibration sensor, a voltage conditioning element, an AD conversion element, a clock element, a power element, a central processing unit, a communication unit and a failure diagnosis upper computer. According to mechanical vibration signals in the motion process of the circuit-breaker, the vibration sensor, the voltage conditioning element, the AD conversion element, the clock element, the power element, the central processing unit, the communication unit and the failure diagnosis upper computer are utilized for achieving the mechanical characteristic failure diagnosis of the circuit-breaker. The method for diagnosing mechanical characteristic failures comprises the steps of conducting wavelet packet decomposition on vibration signals in the operation process of the high-voltage circuit-breaker, extracting characteristic vectors of the vibration signals in spectral entropy of each frequency band, and adopting a relevance vector machine algorithm to conduct failure diagnosis on the mechanical characteristics of the high-voltage circuit-breaker. The method and device can effectively diagnoses the mechanical characteristic failures of the circuit-breaker, and provide a basis for the state maintenance of the circuit-breaker.
Owner:STATE GRID CORP OF CHINA +1
Fault diagnosis method and device for rolling bearing of running gear of locomotive
InactiveCN104807639AHigh speedImprove accuracyMachine bearings testingCharacter and pattern recognitionVibration accelerationNaive Bayes classifier
The invention discloses a fault diagnosis method and device for a rolling bearing of a running gear of a locomotive. The method includes the steps of collecting vibration acceleration data of the rolling bearing of the running gear of the locomotive under different fault types, and grouping the data according to the fault types; obtaining frequency domain signals of the vibration acceleration data according to the grouped vibration acceleration data; conducting three-layer wavelet packet decomposition on the frequency domain signals, and constructing fault characteristic sets; randomly arranging the fault characteristic sets, using the front B sets as the training sets, and using the rear C sets as the testing sets, wherein the sum of B and C is equal to A, and B is larger than C; training the B training sets through a Naive Bayes classifier, and establishing fault diagnosis model based on Naive Bayes for the rolling bearing of the running gear of the locomotive; classifying the C testing sets according to the fault diagnosis model, and evaluating the classification performance of the fault diagnosis model according to the classification result and through the combination with the fault characteristic sets.
Owner:GUANGXI UNIV
High-voltage circuit breaker mechanical state monitoring and fault diagnosis method
InactiveCN109061463ASimple and feasible fault simulationSolve the shortcomings of less fault dataVibration measurement in solidsCircuit interrupters testingRelative energyPrincipal component analysis
The invention discloses a high-voltage circuit breaker mechanical state monitoring and fault diagnosis method. The method comprises the following steps of 1) according to the historical fault data statistics result of high-voltage circuit breakers, carrying out an artificial fault simulation experiment on some common high-voltage circuit breakers to obtain fault data; 2) analyzing the fault current data, and extracting the current data characteristic quantity of each mechanism fault to be used as one of state diagnosis classification basis; 3) analyzing a fault vibration signal, adopting wavelet packet decomposition and sample entropy for processing high-frequency and low-frequency components to obtain corresponding wavelet packet relative energy and sample entropy respectively to be usedas vibration signal characteristic quantities; and 4) performing dimensionality reduction on the vibration signal characteristic quantities through principal component analysis to be used as one of state diagnosis classification basis, and carrying out state diagnosis on the circuit breaker by a support vector machine. By adoption of the method, the multi-dimensional information of the fault statecan be effectively utilized, so that the development of multi-parameter multi-dimensional mapping fault diagnosis of the circuit breaker can be promoted.
Owner:SOUTH CHINA UNIV OF TECH
Method for predicting wind speed and power of wind farm based on wavelet decomposition and support vector machine
ActiveCN105184391AObviously each frequency componentImprove accuracyBiological neural network modelsForecastingSupport vector machineElectricity
The invention discloses a method for predicting wind speed and power of a wind farm based on wavelet decomposition and a support vector machine. The method comprises: acquiring wind speed and power historical data of a whole wind farm in a preset time, to obtain a historical wind speed time sequence and a historical power time sequence of the wind farm; using a wavelet packet decomposition technology to perform wavelet packet decomposition on the historical wind speed time sequence, to obtain a low-frequency stage component, a middle-frequency stage component, and a high -frequency stage component of the historical wind speed time sequence; using a grey support vector machine prediction model to predict each component of the historical wind speed time sequence, and then using wavelet packet reconstruction to obtain short-period wind speed prediction data; using historical wind electricity power data and numerical weather prediction wind speed data as a training set to establish a grey support vector machine model, predicting wind electricity power; predicting the obtained wind speed prediction data and the wind electricity power prediction data through a RBF neural network, to obtain a final prediction value of the wind electricity power.
Owner:ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +2
Voltage sag source identification method based on Hilbert-Huang transformation and wavelet packet energy spectra
InactiveCN103424600AReduced ability to recognizeImprove recognition efficiencyCurrent/voltage measurementDecompositionElectric power system
The invention discloses a voltage sag source identification method based on Hilbert-Huang transformation and wavelet packet energy spectra, and belongs to the field of electric energy quality analysis of electric power system. The method includes the steps: firstly, performing EMD (empirical mode decomposition) for a three-phase voltage signal to obtain IMF (intrinsic mode function) components; secondly, performing n-layer wavelet packet decomposition for the IMF components to obtain energy spectra of frequency bands; thirdly, setting a larger wavelet packet coefficient of low-frequency band energy into zero, and reconstructing wavelet packet coefficients corresponding to other frequency bands to obtain an IMF without low-frequency illusive components; finally, performing Hilbert transformation on the IMF without the illusive components to obtain an HH (Hilbert-Huang) spectrogram, and identifying voltage sag sources according to catastrophe points, amplitudes, harmonic waves and frequencies of the HH spectrogram. Different characteristics of different voltage sag sources can be identified from the HH spectrogram only by taking the amplitudes, the catastrophe points and the harmonic waves as characteristics, the voltage sag sources are differentiated, so that times of non-identification and mistaken identification are effectively decreased, and identification efficiency is improved.
Owner:KUNMING UNIV OF SCI & TECH
Wireless sensor network fault diagnosis method
InactiveCN101867486AGuaranteed accuracyNetwork topologiesData switching networksFeature vectorRelational model
The invention discloses a wireless sensor network fault diagnosis method, which comprises the following steps of: acquiring link quality signals of a wireless sensor network in a normal state and a fault state; performing wavelet packet decomposition and coefficient reconstruction by using a wavelet transform method, and extracting a characteristic vector of a fault; constructing a coding matrix by adopting an error correcting output coding method to realize network fault multi-classification; constructing a decision function of multi-classification problems by using an LSSVC learning mechanism, and establishing a relationship model between the system state and the characteristic vector; and identifying, diagnosing and processing a generated fault, a potential fault type and an area according to a network running decision function value. The method has wide application prospect in small and medium-scale WSN application system.
Owner:JIANGSU UNIV
Probabilistic neural network-based tolerance-circuit fault diagnosis method
ActiveCN101819253AOvercome lossOvercoming structural complexityAnalog circuit testingBiological neural network modelsFault toleranceWavelet
Owner:HUNAN UNIV
Method for estimating reliability of numerical control machine tool cutting tool based on logistic model
InactiveCN102176217AReflect dynamic operating characteristicsSpecial data processing applicationsNumerical controlEngineering
The invention provides a method for estimating the reliability of a numerical control machine tool cutting tool based on a logistic model. The method comprises the following steps: 1, signal analysis and characteristic extraction, in which, a vibration signal in a turning tool processing process and corresponding abrasion loss are measured through experiments, and wavelet packet decomposition andreconstruction are carried out on the vibration signal by utilizing an orthonormal wavelet basis function to ensure that coupled multi-carrier vibration signals are decomposed into a plurality of single-carrier vibration signal, and the time domain characteristic of a characteristic frequency band is calculated; related analysis is carried out on abrasion loss of the turning tool, remarkable indexes of the abrasion loss of the turning tool are extracted, and a failure threshold is determined; and 2, a logistic regression model is established by utilizing the remarkable indexes of the abrasionstate of the turning tool in the step 1, and reliability evaluation indexes and failed service life prediction are carried out on the running turning tool. By the method, the equipment operation status information is introduced to the performance evaluation and reliability analysis, so the dynamic operation characteristic of the equipment can be reflected more accurately.
Owner:XIAN RUITE RAPID MFG ENG
Method for identifying damage on inhaul cables of long-span cable-stayed bridges
InactiveCN108090295AImprove generalization abilityImprove adaptabilityBridge structural detailsDesign optimisation/simulationElement modelAdaBoost
The invention belongs to the technical field of bridges, and discloses a method for identifying damage on inhaul cables of long-span cable-stayed bridges. The method includes building finite element models of cable-stayed bridges; applying simulated wind load to the finite element models and acquiring wind speed time-history curves of the inhaul cables; computing stress time histories of the inhaul cables under wind load effect on the basis of the wind speed time-history curves; carrying out lifting wavelet packet decomposition on the stress time histories and stress time histories of bridge inhaul cables in non-damage states, extracting wavelet packet signal component energy and utilizing energy accumulation variation values as characteristic values for identifying damage positions of thestay cables; acquiring lifting wavelet packet component energy difference, building BP-AdaBoost models for identifying structural damage degrees and computing the fatigue reliability of the inhaul cables on the basis of the structural damage degrees. The method for identifying the damage on the inhaul cables of the long-span cable-stayed bridges has the advantages of excellent adaptability and identification reliability.
Owner:WUHAN OPTICS VALLEY BEIDOU HLDG GRP
X-ray chest radiograph bone suppression processing method based on wavelet decomposition and convolutional neural network
InactiveCN107038692AAchieve inhibitionReduce radiation doseImage enhancementImage analysisX-rayNeural network nn
The invention discloses an X-ray chest radiograph bone suppression processing method based on wavelet decomposition and a convolutional neural network. By adopting a convolutional neural network structure and using a chest radiograph image wavelet coefficient as the input, a wavelet coefficient image of a corresponding bone image or soft tissue image is predicted. The existing bone image or soft tissue image artificial neural network prediction method processes an original chest radiograph image by adopting a relatively complex contrast normalization method, whereas this method processes the input chest radiograph image in a wavelet domain, and can normalize the amplitude by adopting a simple method; and the existing bone image or soft tissue image artificial neural network prediction method needs to design an image feature extraction method as the input of the artificial neural network, whereas this method completes an image feature extraction process by directly using the wavelet decomposition image of the chest radiograph image as an input, training the convolutional neural network to learn automatically and optimizing the convolution kernel, so the image feature extraction method does not need to be designed.
Owner:SOUTHERN MEDICAL UNIVERSITY
Rotating machinery bearing fault diagnosis method
The invention discloses a rotating machinery bearing fault diagnosis method. The method is characterized by, to begin with, carrying out wavelet packet decomposition on an original signal, and extracting first half of wavelet coefficients obtained after wavelet packet decomposition for reconstruction to obtain a reconstruction signal set; then, extracting corresponding characteristic values for reconstruction signals on the time domain and frequency domain, and carrying out pattern recognition by utilizing a BP nerve network to obtain fault classification weight values; fusing the fault classification weight values obtained by the time domain and frequency domain through a D-S evidence theory to obtain a final fusion result; and finally, selecting the maximum value of the fusion result asa decision making result of fault diagnosis, and obtaining a final result of unit diagnosis. The rotating machinery bearing fault diagnosis method not only greatly improves accuracy of fault diagnosis, but also can judge, diagnose and predicate different fault types of a rotating machinery bearing; and the method is better in effect than independent time domain and frequency domain analysis, and can improve fault classification accuracy and stability.
Owner:GUANGDONG UNIV OF PETROCHEMICAL TECH
Method And Apparatus For Reducing Noise In An Image Using Wavelet Decomposition
ActiveUS20080166064A1Reduce noiseImage enhancementImage analysisWavelet decompositionComputer science
A method of reducing noise in an image comprises decomposing the image to generate wavelet coefficients at different scales. The wavelet coefficients are then modified based on the energy of the wavelet coefficients at the different scales. The image is reconstructed based on the modified wavelet coefficients.
Owner:138 EAST LCD ADVANCEMENTS LTD
Aero-engine bearing fault diagnosing method
ActiveCN107036816AImprove effectivenessImprove stabilityMachine bearings testingAviationSupport vector machine
The invention discloses an aero-engine bearing fault diagnosing method. The method comprises: first, acquiring the vibration signal of a turbojet engine, the engine speed, the engine rotation speed, the engine exhaust temperature, the fuel flow, and the engine thrust; extracting the classical time-domain statistical characteristics, the frequency-domain characteristics of the various signals and obtaining the time-frequency domain characteristics based on wavelet packet decomposition; then using the deep Boltzmann machine to perform characteristics learning to the characteristic parameters of the various signals; then, proceeding to use the data integration technology to integrate the characteristics of the various sources learned by the deep Boltzmann machine; and finally, using the integrated characteristic parameters as the input of the classifier to the support vector machine, and classifying and identifying the engine fault. The method of the invention improves the identification precision and stability of the turbojet engine bearing fault, effectively mining the essential characteristics of data and greatly increasing the fault precision of the turbojet engine bearing.
Owner:CHONGQING TECH & BUSINESS UNIV
Linear frequency modulation (FM) signal parameter estimation method based on small-wave-packet denoising and power spectral entropy
InactiveCN102508206ASmall amount of calculationReal-time estimateWave based measurement systemsSignal-to-noise ratio (imaging)Curve fitting
The invention aims at providing a linear frequency modulation (FM) signal parameter estimation method based on small-wave-packet denoising and power spectral entropy, which includes the following steps: denoising signals in a multi-dimension small-wave-packet mode, and determining small-wave-packet function and small-wave-packet decomposition level; calculating power spectral entropy of signals denoised in a small-wave-packet mode, and setting an entropy feature data base of linear FM signals with different FM slope under the condition of different signal to noise ratios; carrying out interpolation operation for obtained discretized entropy features; fitting the curve after interpolation of a cubic spline function with a polynomial function, and obtaining FM slop of linear FM signals under the condition of different signal to noise ratios and relational expression with input entropy features; and estimating FM slop of linear frequency modulation (LFM) signals received by a receiver by utilizing the fit expression. The linear FM signal parameter estimation method based on small-wave-packet denoising and power spectral entropy is small in calculated amount and capable of estimating FM slop of LFM in real time under the premise of meeting the requirement for parameter estimation accuracy.
Owner:HARBIN ENG UNIV
Module transfer method based on multiscale modeling
InactiveCN103854305AImprove efficiencyImprove performanceColor/spectral properties measurements3D modellingMultiscale decompositionMultivariate calibration
The invention discloses a module transfer method based on multiscale modeling. The method comprises the following steps: collecting original spectra of a host instrument and a slave instrument; by combining the characteristic of wavelet basis and the characteristic of the original spectrum of a sample, selecting the optimal wavelet basis to carry out wavelet multiscale decomposition on the spectrum to obtain wavelet coefficient; reconstructing the wavelet coefficient; respectively performing multivariate calibration on each layer of reconstructed spectrum; setting up a prediction model based on partial least squares method and leaving one cross-validation method for the spectrum after performing multivariate calibration to obtain a cross-validation root-mean-square error of the prediction model; performing model fusion on the prediction model by using weight number, and calculating the prediction root-mean-square error and related coefficient to evaluate the model transfer effect. Compared with a conventional model transfer method, the method greatly improves the model transfer efficiency and performance, and can be widely applied to fields of near-infrared and Raman spectrum.
Owner:TIANJIN UNIV
Island detection method based on wavelet packet energy spectrum
InactiveCN102253283AAccurate detection of operating modeTest mode of operationSpectral/fourier analysisIslandingAlgorithm
The invention relates to an island detection method based on the wavelet packet energy spectrum. In the method, the wavelet theory is applied to decompose the signal into different frequency components, so that the fault information can be obviously displayed. The method is characterized by comprising the stages of signal processing and island identification and comprising the following specific steps: 1) directly measuring the voltage signal at the point of common coupling (PCC) and carrying out wavelet packet decomposition on the detected voltage signal; 2) reconfiguring the wavelet packet decomposition coefficient and extracting the signals in the frequency band ranges; 3) computing the energy characteristic value of each frequency band signal according to the energy expression shown in the specification; 4) constructing the fault characteristic vector T with number of dimensions being 2<j> by adopting the energy of the frequency band signals as the elements; 5) establishing a sample model and then determining the fault characteristic vector P according to the statistical average of the sample space; and 6) establishing mapping relationships between the energy characteristic vectors and the fault conditions, making a fault query table with the congruent relationships between the fault conditions and the parameter variation and storing the fault query table, identifying the faults based on the fault query table and simultaneously displaying the diagnosis results.
Owner:STATE GRID SHANDONG ELECTRIC POWER +1
Composite optical fiber sensing system and sensing method
ActiveCN105067104AHigh positioning accuracyHigh frequency resolutionSubsonic/sonic/ultrasonic wave measurementUsing wave/particle radiation meansRayleigh scatteringMoving average
The invention discloses a composite optical fiber sensing system and a sensing method. By adopting the system and the method, the same positioning precision as phase-sensitive OTDR can be achieved. Moreover, high-frequency details of vibration can be obtained, the type of vibration can be identified accurately, and the false alarm rate of the system is effectively reduced. The phase-sensitive OTDR carried out detection by using reverse Rayleigh scattered light, the intensity of reflected light is extremely low, and the light intensity power of an interferometer is high. Therefore, light signals of the phase-sensitive OTDR can be easily submerged in signals of the interferometer. By introducing a FBG, the system successfully combines light paths and separates light signals of the interferometer and a scatterometer, and the system is relatively simple in structure and low in cost. By performing an improved moving average algorithm and a wavelet information entropy algorithm on Rayleigh scattered signals of a laser light source with ultra narrow line width, the accurate position of vibration can be worked out. Interference signals of the laser light source with ultra narrow line width contain rich frequency information, and through wavelet packet decomposition on the interference signals, the type of vibration can be identified via a neural network. The rate of false alarm is reduced.
Owner:天津精仪精测科技有限公司
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