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114 results about "Permutation entropy" patented technology
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Rolling bearing fault diagnosis method based on variation mode decomposition and permutation entropy
InactiveCN105758644AImplement fault diagnosisImprove recognition accuracyMachine bearings testingCharacter and pattern recognitionSupport vector machineDecomposition
The invention relates to a rolling bearing fault diagnosis method based on variation mode decomposition and permutation entropy. Vibration signals are decomposed with a variation mode decomposition method, so that reactive components and mode aliasing are effectively reduced, all the mode components include characteristic information of different time scales of original signals, and effective multi-scale components are provided for subsequent signal characteristic extraction. With the combination of the features that permutation entropy is simple in calculation, high in noise resisting ability and the like, bearing fault characteristics of all the mode components are extracted from multi-scale angles. Compared with single permutation entropy analysis of rolling bearing vibration, the characteristic information of the signals can be more comprehensively represented through the permutation entropy characteristic extracting method based on multiple scales, the recognition accuracy of a support vector machine is improved, and fault diagnosis of rolling bearings is better achieved.
Owner:SHANGHAI UNIVERSITY OF ELECTRIC POWER
Rolling bearing fault diagnosis method based on composite multi-scale permutation entropies
ActiveCN104849050AEfficient extractionHighly innovativeSubsonic/sonic/ultrasonic wave measurementMachine bearings testingSupport vector machineFeature vector
The invention discloses a rolling bearing fault diagnosis method based on composite multi-scale permutation entropies, and belongs to the technical field of fault diagnosis. The method comprises the following steps: measuring a vibration signal of a faulty object; extracting composite multi-scale permutation entropies from the vibration signal; reducing the dimension of the composite multi-scale permutation entropies with use of a Laplacian score; taking the first multiple composite multi-scale permutation entropies with low scores after dimension reduction as fault feature vectors and dividing the fault feature vectors into multiple training samples and multiple test samples; inputting the multiple training samples into a multi-fault classifier established based on a support vector machine to perform learning so as to classify the test samples; and identifying the working mode and the fault type of the faulty object according to the classifying result. According to the fault diagnosis method disclosed by the invention, feature extraction is highly innovative, and the degree of identification is high in the process of fault mode identification.
Owner:ANHUI UNIVERSITY OF TECHNOLOGY
Vibration fault diagnosis method of hydroelectric generating set
InactiveCN105004498AEnsure safetyGuaranteed economyBiological modelsVibration testingSupport vector machineFeature vector
The invention relates to a vibration fault diagnosis method of a hydroelectric generating set. The method comprises the steps of: firstly, carrying out denoising processing on collected original vibration signals by utilizing a random resonance technology; secondly, carrying out characteristic vector extraction on the vibration signals after the denoising by utilizing a multi-dimensional permutation entropy technology; thirdly, establishing fault diagnosis models of optimized support vector machine based on an improved particle swarm algorithm; fourthly, inputting extracted characteristic vectors into the models of the models of optimized support vector machine based on the improved particle swarm algorithm for fault diagnosis. The method is applicable to the vibration fault diagnosis of the hydroelectric generating set, the diagnosis result is high in precision, the fault type of the generating set can be relatively accurately diagnosed, the reliable diagnosis result is provided to operation maintenance personnel of the generation set, the maintenance personnel can process faults timely and rapidly, and the safety and the economic performance of the operation of the generating set are ensured.
Owner:XIAN UNIV OF TECH
Method for identifying a microearthquake event with low signal-to-noise ratio based on multi-scale permutation entropy
InactiveCN105956526AEfficient analysisAccurate classificationCharacter and pattern recognitionSeismic signal processingSignal-to-noise ratio (imaging)Algorithm
The invention discloses a method for identifying a microearthquake event with a low signal-to-noise ratio based on multi-scale permutation entropy. The method is performed by a computer and comprises steps of: processing acquired microearthquake data to convert the microearthquake into time series data, coarse graining a time series to obtain a multi-scale time series, and computing multi-scale time series permutation entropy; training a least squares support vector machine (LS-SVM) on the basis of the multi-scale time series permutation entropy, and identifying a signal to be identified by using the trained LS-SVM. The method may analyze signal features by applying multiple scales and the permutation entropy to the microearthquake signals, accurately expresses the waveform characteristics in multiple dimensions of the microearthquake signals, and is beneficial to discrimination between the microearthquake event and a noise event. The method extracts characteristic data of the microearthquake signals and the noise signals by using the multi-scale permutation entropy, trains the characteristic data by using the LS-SVM to obtain the LS-SVM, and accurately classify the microearthquake event with a low signal-to-noise ratio and the noise event.
Owner:SHANDONG UNIV OF SCI & TECH
Rolling bearing fault diagnosis method based on improved multi-scale amplitude perceived permutation entropy
ActiveCN109916628AGood divisibilityGood fault severity description abilityMachine part testingDigital signal processingFeature vector
The invention provides a rolling bearing fault diagnosis method based on improved multi-scale amplitude perceived permutation entropy, relates to the field of digital signal processing, and aims at solving the problems of low separability of feature extraction, low accuracy of fault recognition and insufficient analysis of fault severity in the existing fault diagnosis method of the rolling bearing vibration signal. The method comprises the following steps: step 1. obtaining vibration signal sample sets of the rolling bearing under different fault types and different fault degrees; step 2. obtaining the optimal PR component for subsequent feature extraction; step 3. obtaining fault feature vectors of different fault types and different fault degrees; step 4. inputting the feature vector into the random forest classifier; and step 5. obtaining the fault type and the fault severity of the rolling bear. The extracted feature vector has good separability and high fault description ability,and the average recognition accuracy rate reaches 99.25%. The method can be widely applied to the field of bearing fault diagnosis.
Owner:哈尔滨科速智能科技有限公司
Partial discharge type identification method based on synchrosqueezing wavelet transform
InactiveCN108983051AImprove stabilityImprove anti-interference abilityTesting dielectric strengthTransformerDecomposition
The invention provides a partial discharge type identification method based on synchrosqueezing wavelet transform, and aims to provide an identification method for the partial discharge signal of a transformer to realize the identification of the partial discharge signal of the transformer. The method comprises first, decomposing the typical partial discharge signal of the transformer by using thesynchrosqueezing wavelet transform; then, using a multi-scale permutation entropy as a feature quantity of the discharge type identification by using the energy and complexity difference of the partial discharge signal at different decomposition scales; and finally, identifying a discharge type by using a support vector machine classifier based on the extracted feature quantity. The method can achieve average partial discharge signal identification accuracy higher than 90%, and is obviously superior to other commonly used transformer partial discharge identification methods.
Owner:WUHAN UNIV OF SCI & TECH
Seismic signal random noise suppression processing method
InactiveCN108267784AAvoid problems that are largely influenced by subjective factorsImprove problems such as poor denoising effectSeismic signal processingSingular value decompositionRandom noise
Owner:STATE GRID JIANGXI ELECTRIC POWER CO LTD RES INST +1
Lower limb motion mode identification method integrated with surface electromyography and acceleration signals
InactiveCN107832686AStrong ambiguityImprove classification accuracyBiometric pattern recognitionExoskeleton robotDecomposition
The invention provides a lower limb motion mode identification method integrated with surface electromyography and acceleration signals. The method comprises steps that firstly, the surface electromyography and acceleration signals of a lower limb of a human body are acquired; the surface electromyography signal is decomposed through utilizing a local mean decomposition algorithm to acquire multiple product functions, according to the average Euclidean distance representing separation of different motions, the multi-scale permutation entropy of the first product function after decomposition through the local mean decomposition algorithm is determined, and the multi-scale permutation entropy of the first product function is extracted as surface EMG signal characteristics; importance of theentropy at different scales is calculated, the scale entropy is determined to form a 4-dimensional characteristic vector, and the 4-dimensional characteristic vector and a triaxial acceleration sequence entropy form a 7-dimensional characteristic vector; the 7-dimensional characteristic vector is inputted to a binary tree support vector machine improved according to the intra-class average Euclidean distance and inter-class sample distribution to carry out lower limb motion mode identification. The method is advantaged in that human body lower limb motion intention can be accurately identifiedin real time, and the identification result can be utilized for exoskeleton robot interaction control.
Owner:HANGZHOU DIANZI UNIV
Rolling bearing fault diagnosis method and system, storage medium, equipment and application
PendingCN112347854AMachine part testingCharacter and pattern recognitionFeature vectorNeighborhood search
The invention belongs to the technical field of bearing vibration signal identification, and discloses a rolling bearing fault diagnosis method and system, a storage medium, equipment and application,and the method comprises the steps: collecting original signals of a bearing in four states, carrying out the signal decomposition through VMD, and obtaining all IMF components; extracting signal features by using multi-scale permutation entropy, constructing a feature vector set, and dividing the feature vector set into a training sample and a test sample; initializing a whale algorithm population scale, an iteration frequency and an adaptive weight value; establishing an LSSVM model by using the initialization parameters; calculating a fitness value corresponding to each whale, and sortingthe whale according to the fitness; carrying out neighborhood search by adopting a von Noemann topological structure, carrying out information exchange in a neighborhood, finding an optimal whale in the neighborhood, and carrying out position updating according to a formula; and outputting the whale position with the optimal fitness as the parameter of the LSSVM for training, and carrying out fault classification on the test set. The method is better in fault classification performance and higher in accuracy.
Owner:XIDIAN UNIV
Signal feature extraction method used for distributed optical fiber vibration sensing system
InactiveCN107885940AResolve confusionImprove accuracyDesign optimisation/simulationSpecial data processing applicationsOriginal dataDecomposition
The invention discloses a signal feature extraction method used for a distributed optical fiber vibration sensing system. The method mainly comprises the steps of in an improved ensemble empirical mode decomposition (MEEMD) processing process, reading original data, and performing vibration signal locating and phase demodulation; introducing two groups of white noises with a mean value of zero toperform EMD; performing permutation entropy calculation for a first IMF component; comparing an entropy value with a set threshold, and if the entropy value is higher than the set threshold, repeatingthe steps until the entropy value is lower than the threshold; performing EMD on residual data to obtain residual IMF components of a vibration signal; and performing Hilbert analysis on the IMF components to obtain an eigenvector of vibration signal mode identification. By applying the method provided by the invention, the problems of mode mixing, false components and the like in a conventionaldecomposition method can be solved; the processing process is simplified; the reconstruction precision is improved; the data processing time is shortened; and the method is of important significance for improving the mode identification timeliness and accuracy of the distributed optical fiber vibration sensing system.
Owner:JILIN UNIV
Abnormal public place sound feature extraction and recognition method
ActiveCN106228979AEasy to identifyReduce decomposition biasSpeech recognitionSequence signalPublic place
The invention relates to an abnormal public place sound extraction and recognition method for improving extreme-point symmetric mode decomposition (ESMD), abbreviated as D-ESMD. The method is characterized in that random T distribution sequence signals are added to abnormal public place sound, thereby reducing the influence of public place background noise on abnormal sound feature extraction; aiming at the problem of poor decomposition effect of original ESMD when abnormal sound is decomposed, a method for replacing extreme midpoint odd-even interpolation with symmetric midpoint interpolation is put forward to improve the decomposition efficiency and recognition rate of abnormal sound; aiming at the defects of original ESMD on effective decomposition mode selection, a permutation entropy algorithm is put forward to detect the complexity of the mode obtained by ESMD decomposition, so that an effective mode component of abnormal sound is adaptively obtained. By using the method, the feature of abnormal sound can be sufficiently described, good class recognition results can be obtained, and the feature of abnormal sound can be accurately extracted; and the method has good robustness on environmental background noise.
Owner:CHONGQING UNIV
Electric energy quality analysis method based on variational mode decomposition multi-scale permutation entropy
InactiveCN110111015ARealize real-time monitoringEfficient separationTechnology managementResourcesPower qualityFeature vector
The invention discloses an electric energy quality analysis method based on variational mode decomposition multi-scale permutation entropy. The method comprises the steps of collecting original training data of electric energy quality monitoring points when the electric energy quality monitoring points are disturbed by different types; adopting variational mode decomposition to decompose the components to obtain K IMF components; calculating the multi-scale permutation entropy of each IMF component, and constructing a feature vector of the original training data; selecting R features from thefeature vectors to form an optimized feature vector of the original training data; taking the optimized feature vector of the original training data and the corresponding disturbance type as input data and output data respectively, and training an ELM neural network model to obtain an electric energy quality disturbance classifier; and acquiring optimized feature vectors of voltage signals of to-be-detected power quality monitoring points according to same method; inputting the optimized feature vectors into the power quality disturbance classifier to obtain the disturbance type of the power quality monitoring point to be detected. According to the method, the real-time diagnosis efficiency of the disturbance type of the power quality is greatly improved.
Owner:CENT SOUTH UNIV
An intelligent rotary machine fault depth network feature identification method
ActiveCN109583386AImprove accuracyIncrease training rateMachine part testingCharacter and pattern recognitionSupport vector machineSingular spectrum analysis
The invention discloses an intelligent rotary machine fault depth network feature identification method. A vibration sensor is arranged at a to-be-detected rotating mechanical part of a train rollingbearing; collecting an original vibration sequence when the rolling bearing works; decomposing and reconstructing the original vibration sequence through a singular spectrum analysis method; extracting a root-mean-square value of the reconstructed vibration sequence; standard deviation, skewness index and peak value; a fault position is judged by using a rotary machine fault position diagnosis model obtained by training of a support vector machine; and then, carrying out ensemble empirical mode decomposition on the reconstructed vibration sequence, calculating the permutation entropy values ofa group of decomposed intrinsic mode components, taking the permutation combination of the permutation entropy values as a detection characteristic, and judging the fault type by using a rotary machine fault type diagnosis model obtained by training of a support vector machine. The fault position and the fault type of the rotary machine can be detected more timely, and the fault diagnosis accuracy and reliability are improved.
Owner:CENT SOUTH UNIV
Method and system for noise reduction of ground penetrating radar B-scan image based on EEMD and permutation entropy
ActiveCN107480619ASolve the problem of signal mode aliasingReduce noiseImage enhancementImage analysisDecompositionNoise reduction
The present invention discloses a method and a system for noise reduction of a ground penetrating radar B-scan image based on EEMD and permutation entropy. First, a ground penetrating radar two-dimensional B-scan image signal is obtained, and then each obtained B-scan image signal is subjected to noise reduction to obtain the B-scan image signal of the channel after the noise suppression signal, and finally the signal after each channel is recombined to obtain a noise-suppressed two-dimensional B-scan image signal; for any one B-scan image signal, the method for denoising includes: performing EEMD decomposition on the B-scan image signal of the channel to obtain K IMF components arranged from high frequency to low frequency; calculating arrangement entropy values of each IMF component; and selecting an arrangement entropy value not greater than a preset value IMF components to reconstruct, and obtaining the signal after denoising. The invention solves the signal mode aliasing problem existing in the EMD decomposition and can effectively reduce the noise.
Owner:CHINA UNIV OF GEOSCIENCES (WUHAN)
An Improved Fault Diagnosis Method of Limit Learning Machine Based on Information Reconstruction
InactiveCN109299727AImprove applicabilityEffective diagnosisCharacter and pattern recognitionLearning machineFeature extraction
The invention relates to an improved fault diagnosis method of a limit learning machine based on information reconstruction, which comprises the following steps: 1) collecting signals; 2) signal processing; 3) feature extraction; 4) division of fault diagnosis; the invention is based on the permutation entropy (PE) thought, and proposes the weighted permutation entropy (WPE) thought. By weightingthe entropy characteristic of the common permutation entropy, the characteristic information becomes more sensitive, the change of the characteristic information can be well presented, and the foundation for the characteristic selection is provided. In addition, a new method based on Filter-Wrapper (filter-package) method discriminates the features effectively, minimizes the error by constantly adjusting the output weight of the network, minimizes the error of the output result of the limit learning machine, and compares the result with the result of the traditional limit learning machine, thereby verifying the effectiveness of the invention.
Owner:LIAONING UNIVERSITY
Bearing fault quantitative trend diagnosis method based on morphology and multi-scale permutation entropy mean value
ActiveCN108982107AMachine bearings testingComplex mathematical operationsRelational graphEngineering
The invention discloses a bearing fault quantitative trend diagnosis method based on morphology and a multi-scale permutation entropy mean value. When the fault size of the inner ring or the outer ring of a bearing is changed, the modulation degree of the vibration signal of the bearing is changed. The change affects the complexity and randomness of the vibration signal. The method, by using the superiority of the multi-scale permutation entropy in the aspect of representing the degree of uncertainty of vibration signals, draws a relational graph of the multi-scale permutation entropy mean values and the fault sizes, and then realizes quantitative trend diagnosis of the rolling bearing faults. The vibration signal acquired by an experiment contains serious noise and a large number of interference signals. In order to remove the noise interference and enhance the impact performance of the vibration signal, the multi-scale morphology is introduced into the method so as to greatly improvethe accuracy of rolling bearing fault quantitative trend.
Owner:BEIJING UNIV OF TECH
Method for detecting nonlinear oscillation during industrial process based on improved variational mode decomposition
ActiveCN109542089AEasy to detectImplement diagnosticsElectric testing/monitoringPhase correctionFrequency spectrum
The invention discloses a method for detecting a nonlinear oscillation during an industrial process based on improved variational mode decomposition. The method comprises the following steps: (1) collecting a set of loop output signals of a to-be-detected industrial process; (2) calculating a frequency spectrum and a phase correction signal mean frequency spectrum of the loop output signals to determine the mode number and the center frequency initial value; (3) setting the search range and the step size of a penalty coefficient; (4) calculating the sum permutation entropy obtained through a VMD decomposition corresponding to different penalty coefficients, and determining an optimal penalty coefficient; (5) performing the VMD decomposition adopting the determined mode number, the center frequency initial value and the penalty coefficient to select an effective mode; and (6) calculating whether a multiple relation exists between center frequencies of the effective mode, and judging whether a nonlinear oscillation exists or not. According to the method for detecting the nonlinear oscillation during the industrial process based on improved variational mode decomposition, the accuracyand the reliability of a nonlinear detection for the control loop of the industrial process can be improved, a data support is provided for performance evaluations and fault diagnoses, and a foundation is laid for a subsequent positioning work of multi-loop nonlinear oscillation sources.
Owner:ZHEJIANG UNIV
A multivariable time sequence prediction method based on adaptive noise reduction and integrated LSTM
InactiveCN109919364AEfficient separationReduce the impactForecastingNeural architecturesAlgorithmAdaptive denoising
The invention discloses a multivariable time sequence prediction method based on adaptive noise reduction and integrated LSTM (Long Short Term Memory). The multivariable time sequence prediction method is used for solving the problems of unstable performance and low prediction precision when a multivariable time sequence with non-stationary, non-linear and noisy characteristics is predicted by anexisting method. The method comprises the following steps: decomposing a noise-containing chaotic multivariable time sequence by adopting a complete set empirical mode decomposition method with adaptive noise to obtain a series of intrinsic mode functions with frequencies from high to low; Distinguishing a noise-containing high-frequency intrinsic mode function from a low-frequency noise-free intrinsic mode function by adopting a permutation entropy thought; Constructing a self-adaptive threshold value and a self-adaptive threshold value function to reduce noise of the noisy intrinsic mode function; Constructing a stacked automatic encoder to extract characteristics of the multivariable time sequence after noise reduction; Constructing a multivariable time sequence weak predictor based onthe LSTM neural network; And constructing an integrated algorithm considering the prediction error of the verification set, and combining a plurality of LSTM weak predictors to obtain a strong predictor.
Owner:SOUTH CHINA UNIV OF TECH
High speed train wheel set bearing fault diagnosis method based on MEEMD permutation entropy
InactiveCN108254179AReduce residual noiseSuppresses modal aliasing issuesMachine bearings testingDecompositionEngineering
The invention provides a high speed train wheel set bearing fault diagnosis method based on MEEMD permutation entropy for the disadvantages of EMD and EEMD. The high speed train wheel set bearing fault diagnosis method based on the MEEMD permutation entropy comprises the following steps in turn: signal acquisition; filtering and denoising of the original vibration signal; MEEMD decomposition; permutation entropy feature extraction; dividing the high-dimensional feature vectors into two groups; model training; and diagnosis result. In the feature extraction link, the signal features are reflected on multiple dimensions by introducing the MEEMD, and the relative single fault mode identification rate is obviously enhanced in comparison with EMD permutation entropy feature identification rate.The data required for the analysis method based on the MEEMD permutation entropy are short, and the anti-noise and anti-interference capacity is high so that the method can be effectively applied tohigh speed train wheel set bearing fault analysis.
Owner:CHANGZHOU LUHANG RAILWAY TRANSPORTATION TECH
Time sequence complexity measurement method based on image micro-structure frequency analysis
InactiveCN106503660AEnrich and expand the concept of entropyMeasuring complexityCharacter and pattern recognitionMicro structureTime–frequency analysis
The invention provides a time sequence complexity measurement method based on the image micro-structure frequency analysis. The method comprises the steps of constructing a signal recursive matrix; drawing a gray image according to the recursive matrix at the recursive state of the i moment and at the recursive state of the j moment; filtering the gray image according to Gaussian kernel functions of different scales so as to obtain different Gaussian gray images and form a Gaussian pyramid; subjecting the Gaussian kernel functions of different scales and the gray images to the convolution operation to obtain the scale space of the images; in the above scale space, preliminarily determining the positions and the scales of feature points; conducting the least-squares fitting based on the secondary expansion equation of the Taylor function of a scale-space function, and removing unstable feature points by utilizing the extreme values of a fitting surface; clustering remaining feature points; continuously changing the values of an influence degree and a validity degree through calculating; subjecting an obtained clustering result to information measurement; calculating the complexities of different signals by using an approximate entropy and a permutation entropy; and subjecting the results of microstructure recursive entropies to comparative analysis. The above method provides a beneficial reference for the design of novel entropy methods.
Owner:TIANJIN UNIV
Concealed attack detection method for industrial control on-site equipment
ActiveCN108388233AImprove the level of security protectionStealth attack worksProgramme controlElectric testing/monitoringComputer scienceIndustrial control system
The invention provides a concealed attack detection method for industrial control on-site equipment, and the method can achieve the effective detection of concealed attacks. The method comprises the steps: comparing observation output of an industrial control object with predicted expected output, obtaining a residual error sequence, and carrying out the residual error preprocessing; calculating apermutation entropy of the residual error sequence; determining that the industrial control object encounters the concealed attack if the descend range of the permutation entropy of the residual error sequence is greater than a preset first threshold value in a preset time interval, or else determining that the industrial control object does not encounter the concealed attack. The method is suitable for the concealed attack detection of the industrial control on-site equipment.
Owner:UNIV OF SCI & TECH BEIJING
Power quality disturbance detection method for power distribution network based on improved EWT and CMPE
ActiveCN110441654AAccurate detection of disturbance amplitudeAccurate classificationCharacter and pattern recognitionFault location by conductor typesPower qualityDimensionality reduction
The invention discloses a power quality disturbance detection method for a power distribution network based on improved EWT and CMPE, and the method comprises the steps of decomposing a PQ disturbancesignal of an active power distribution network system by adopting improved empirical wavelet decomposition EWT, filtering noise of the PQ disturbance signal, and decomposing to obtain an EWT component containing characteristic information; using the EWT component containing the characteristic information an input signal of a composite multi-scale permutation entropy CMPE algorithm, carrying out permutation entropy calculation on each EWT component containing the characteristic information by utilizing the composite multi-scale permutation entropy CMPE algorithm, and calculating an entropy value matrix of each PQ disturbance signal under each mode function; using a PCA algorithm to perform dimensionality reduction on the calculated entropy value matrix, calculating principal component components, and obtaining characteristic ranges of various PQ disturbance signals; according to the obtained eigenvalue matrix after the dimensionality reduction processing, using the eigenvalue matrix asthe input quantity of a SVM algorithm; and identifying the PQ disturbance signal of the active power distribution network system containing the distributed energy. The power quality disturbance detection method for the power distribution network based on improved EWT and CMPE is simple in steps and accurate in classification, and can improve the reliability of the power distribution network.
Owner:CHINA THREE GORGES UNIV
Classification system of EEG signals in different anesthesia conditions
InactiveCN108451527AImprove classification accuracyReduce distractionsDiagnostic recording/measuringSensorsDeep anesthesiaTime domain
The invention discloses a classification system for EEG signals in different anesthesia states. The classification system comprises an EEG signal acquisition module, a front-end signal processing module, a frequency domain and time domain parameter calculation module, an anesthesia depth estimation module and a display module. The characteristics of different anesthesia depths are obtained, and the obtained characteristic values are divided into clear anesthesia, superficial anesthesia, normal anesthesia and deep anesthesia. The invention introduces a brain function index which combines the permutation entropy and the burst suppression ratio to analyze the complex nonlinear random signal of the EEG signal in the frequency domain and the time domain, thereby improving the classification accuracy of the deep anesthesia. The classification method can be used to determine the anesthesia depth of a patient during anesthesia and operation, and provides a reliable basis for the medical staffto perform anesthesia operation on the patient.
Owner:UNIV OF SCI & TECH OF CHINA
Bearing fault signal feature extraction method based on adaptive multiscale AVGH conversion
ActiveCN106500991AAccurate analysisStrong ability to extract fault features from signalsMachine bearings testingFeature extractionOptimal weight
The present invention discloses a bearing fault signal feature extraction method based on adaptive multiscale AVGH conversion. The method comprises the steps: 1, according to the parameter index of the bearing fault signals, determining the number of the initial multiscale structure elements and the initial structure element values of signals; 2, constructing the set formed by the initial multiscale structure elements; 3, calculating the results of the morphology AVG-Hat conversion corresponding to the bearing fault vibration signals in the initial multiscale structure elements, and constructing the set of the results; 4, selecting the specific value of the permutation entropy of the filtered bearing fault vibration signals and the spectrum envelope sparseness as an evaluation index and adaptively determining the optimal weight coefficient corresponding to the filtered initial multiscale structure elements; 5, constructing the optimal multiscale morphology AVG-Hat filter according to the weight coefficient; and 6, calculating the processing result of the bearing fault vibration signals through the filter, and extracting the fault feature components in the signals through the signal spectrum envelope analysis to perform bearing fault diagnosis.
Owner:SHIJIAZHUANG TIEDAO UNIV
OLTC mechanical fault diagnosis method based on MPE and SVM
InactiveCN108362488AImprove diagnostic accuracyGood diagnosisMachine part testingSubsonic/sonic/ultrasonic wave measurementFeature vectorSupport vector machine
The present invention discloses an OLTC mechanical fault diagnosis method based on MPE and SVM. The method comprises the following steps of: 1) performing collection of vibration signals of an on-loadtap-switch (OLTC) in a normal state and vibration signals of the OLTC in fault state through an acceleration sensor to perform preprocessing of the vibration signals; 2) performing multi-scale permutation entropy (MPE) calculation of the collected vibration signals, and constructing feature vectors taken as input of a support vector machine (SVM); and 3) inputting the feature vectors obtained inthe step 2) into the SVM to perform training of the SVM, and inputting test data into the trained SVM to determine a fault mode of the OLTC. The OLTC mechanical fault diagnosis method based on the MPEand the SVM does not need a lot of data for training of the SVM and is higher in diagnosis precision, and is obviously better than a BP neural network in the diagnosis effect of the OLTC.
Owner:HOHAI UNIV
Wind turbine generator fault diagnosis method
ActiveCN110443117ASolve the difficulty of obtaining in large quantitiesSolve the problem of lack of label informationMachine part testingCharacter and pattern recognitionCovarianceEngineering
The invention discloses a wind turbine generator fault diagnosis method, which comprises the following steps: according to the vibration signal characteristics of a wind turbine generator gearbox, carrying out variational mode decomposition on signals under different working conditions to obtain a series of intrinsic mode function components, and respectively solving multi-scale permutation entropies of the intrinsic mode function components; combining the multi-scale permutation entropy and the original signal time domain feature into a feature vector, and inputting the feature vector into atransfer learning algorithm; the covariance of a source domain and a target domain being minimized through a linear transformation matrix, the distribution difference of signal data of the source domain and the target domain being reduced through second-order statistics alignment, and then inputting the feature vectors of the aligned signal data of the source domain and the target domain into a support vector machine for fault classification. According to the method, the problem of poor classification effect caused by different distribution of the vibration signal data under different workingconditions can be solved, and the method has higher accuracy in wind turbine generator fault diagnosis under variable working conditions.
Owner:XUZHOU NORMAL UNIVERSITY
Joint noise reduction method based on variational mode decomposition and permutation entropy
PendingCN110659621APreserve randomnessRetain propertiesCharacter and pattern recognitionAlgorithmTheoretical computer science
The invention discloses a joint noise reduction method based on variational mode decomposition and permutation entropy, and belongs to the technical field of signal processing. The joint noise reduction method comprises the following steps: firstly, reading a noisy signal x (t); calculating the scale index a value of x (t), and determining the decomposition number K of variational mode decomposition (VMD) according to the a value; secondly, performing K-layer VMD decomposition on the noisy signal x (t) to obtain a series of variational mode components uk; then, calculating the permutation entropy of each variational mode component uk, and further determining the value of K; and finally, removing the noise uk component, and reconstructing the remaining uk component to obtain a noise-reducedand filtered signal. The method has the advantages of adaptively determining the decomposition number, effectively removing noise components and being high in robustness and real-time performance, effective noise reduction and filtering processing can be carried out on signals, and the method has good technical value and application prospects for non-stationary signal noise reduction methods.
Owner:SHANDONG UNIV OF SCI & TECH
Method for separating and identifying composite fault features of wind power transmission chain
ActiveCN107525671AAchieve separationImplement diagnosticsMachine gearing/transmission testingElectricityEngineering
A method for separating and identifying composite fault features of a wind power transmission chain includes the following steps of: obtaining a vibration signal by using an acceleration sensor; decomposing the acquired vibration signal in a whole frequency range by using multi-wavelet packet transform; by using a permutation entropy as an evaluation index, selecting single signals satisfying requirements to perform reconstruction to complete signal noise reduction and composite fault separation; and processing the reconstructed signals by using an energy operator demodulation method to identify fault information.
Owner:INST OF ELECTRICAL ENG CHINESE ACAD OF SCI
Calculation order tracking method capable of adaptively reducing noise and avoiding order aliasing
ActiveCN110084208AReduce noise interferenceOvercome the order aliasing problemCharacter and pattern recognitionHigh level techniquesAdaptive denoisingCenter frequency
The invention discloses a calculation order tracking method capable of adaptively reducing noise and avoiding order aliasing, which is characterized by comprising the following steps of: defining a margin frequency according to signal rotating speed information and a predicted maximum analysis order; carrying out VMD pre-decomposition on the signal, reserving a mode of which the center frequency is lower than the margin frequency, and abandoning the mode of which the center frequency is higher than the margin frequency so as to filter out high-frequency noise in the signal and high-order components in a non-analysis order bandwidth; calculating the permutation entropy PE of the reconstructed signal: using the PE for representing the random degree of the time sequence, wherein the larger the value of the PE is, the more random the time sequence is; optimizing the VMD parameter by adopting a differential evolution algorithm to obtain a parameter, and adaptively generating a reconstructedsignal; and calculating a resampling order, carrying out calculation order tracking on the obtained reconstructed signal, and carrying out FFT after obtaining the resampling signal to obtain an orderspectrum of the signal. The method is used for processing an original vibration signal so as to adaptively reduce noise interference in the collected vibration signal and highlight fault information.
Owner:SHANGHAI UNIVERSITY OF ELECTRIC POWER
Personalization anesthesia closed-loop control system
ActiveCN106902421AReal-time automatic monitoringEasy to controlAutomatic syringesMedical devicesPersonalizationLoop control
The invention discloses a personalization anesthesia closed-loop control system. The brain electricity data are obtained by a signal collection end of a brain electricity data acquisition device, then the collected brain electricity data are transmitted to a brain signal pretreatment device for the removal of noises and outliers; then the pretreated brain electricity data are sent by the brain electricity signal pretreatment device to a brain electricity signal analysis device; the brain electricity signal analysis device and the closed-loop control system are connected and the pretreated data are analyzed, the expected anesthesia depth index RPE (Renyi permutation entropy) values are obtained, and the RPE values are transmitted to a closed-loop control system; the closed-loop control system is connected with an injection device and at the same time controls it to work, the next-step anesthetic infusion speed is calculated by the closed-loop control system, after the signals are received by the injection device, and the real time control of the anesthetic infusion speed is completed by the injection device. The system has the advantages of real-time monitoring, automatic control, and high accuracy and the like.
Owner:JIANGSU APON MEDICAL TECHNOLOGY CO LTD
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