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241 results about "Probabilistic neural network" patented technology
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A probabilistic neural network (PNN) is a feedforward neural network, which is widely used in classification and pattern recognition problems. In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric function. Then, using PDF of each class, the class probability of a new input data is estimated and Bayes’ rule is then employed to allocate the class with highest posterior probability to new input data. By this method, the probability of mis-classification is minimized. This type of ANN was derived from the Bayesian network and a statistical algorithm called Kernel Fisher discriminant analysis. It was introduced by D.F. Specht in 1966. In a PNN, the operations are organized into a multilayered feedforward network with four layers...
Human body fatigue evaluation method based on brain waves
The invention discloses a human body fatigue evaluation method based on brain waves. According to the method, a ThinkGearAM electroencephalogram chip is used for acquiring original brain wave signals, an built-in algorithm is used for analyzing and processing the original brain wave signals, and four kinds of parameters are given through calculation according to processed brain wave data; the four kinds of parameters include variable coefficients of five brain wave signals of original delta waves, original theta waves, original alpha waves, original beta waves and original gamma waves, two nonlinear parameters of complexity and power spectral entropy, a fatigue index F worked out through energy of four basic rhythms of the delta waves, the theta waves, the alpha waves and the beta waves in the brain waves, and two parameters of relaxation degree and attention degree extracted through the brain wave signals, and the four kinds of parameters serve as input of a probabilistic neural network (PPN), the output of the PNN serves as a human body fatigue evaluation basis, and therefore the human body fatigue can be judged according to the brain waves of people.
Owner:朱晓斐 +3
Predictive failure analysis for storage networks
InactiveUS6948102B2Improve reliabilityImprove availabilityDigital computer detailsBiological neural network modelsStorage area networkPredictive failure analysis
A method of predicting failures of data storage devices in a data storage network. In predicting failures, birth and / or health records of data storage devices in the data storage network are requested. The records are then scaled and thresholded and processed using a probabilistic neural network to classify the data storage devices. Based on the classification, an action is taken to improve the reliability and availability of the data storage network.
Owner:IBM CORP
Wind turbine generator gear case fault diagnosis method based on VMD and FA_PNN
InactiveCN110595765AVerify validityVerify feasibilityMachine part testingEngineeringFirefly optimization
The invention discloses a wind turbine generator gear case fault diagnosis method based on VMD and FA_PNN. Firstly, gear case vibration signals acquired by a sensor are subjected to de-trending processing, then, the processed gear case vibration signals are subjected to VMD variation modal decomposition under the condition of different decomposition numbers and penalty factors, k modal componentsare obtained with a Pearson's correlation coefficient method, singular value entropy, power spectral entropy, marginal spectral entropy and instantaneous energy spectral entropy of the k modal components are extracted from three angles of time domain, frequency domain and time-frequency domain, a feature vector matrix capable of describing operating states of a wind turbine generator gear case ina quantization manner is formed, and finally, test sample data are tested with well-trained firefly optimized probabilistic neural network FA_PNN, so that fault diagnosis of the wind turbine generatorgear case is completed. Classified recognition of faults of the wind turbine generator gear case is realized.
Owner:XIAN UNIV OF TECH
Electrical energy power quality disturbance automatic identification method and system based on information fusion
InactiveCN101034389ARealize intelligent identificationOvercome the disadvantage of strong subjectivityData processing applicationsData acquisition and loggingPower qualityNerve network
The invention is a automatic identification method and system based on the power quality disturbances of the information fusion, characterized by: collecting the transient and steady-state measurement datum associated with the power quality disturbances from the Power Quality Monitoring System and other automation systems, disposing of noise such as pretreatments; Using the method combining Fourier analysis, small wave multi-resolution decomposition and analysis of the correlation functions, distilling the from the disturbance datum, establishing the disturbance eigenvector, and as a the input characteristic vector of three probabilistic neural networks, realizing the mapping from a feature space to the disturbance space; the output of three probabilistic neural networks regarded as the evidence body of independent of each other, realizing Information Fusion by the use of D-S evidence theory, obtaining recognition results. This invention through the correct selection and extraction of disturbance eigenvectors, can input neural network parallel in classification and reflect the disturbance situation from the various aspects, thus effectively enhancing the correct identification rate of disturbance, a first step in Intelligent Recognition of the power quality disturbances.
Owner:ELECTRIC POWER RES INST STATE GRID JIANGXI ELECTRIC POWER CO
Digital modulation signal identification method under pulse noise
The invention discloses a digital modulation signal identification method under pulse noise. The method comprises the following steps that the fractional low-order fuzzy function of digital modulation signals is calculated; the tangent plane of zero Doppler frequency shift of the fractional low-order fuzzy function is intercepted, and the tangent plane is converted into a two-dimensional image and fills below the edge of the image so as to become a color image with color; graying, binarization, image segmentation and image size normalization are performed on the color image and translation and scale normalization are performed on the image; and the Zernike moment of the image is extracted to act as the characteristic vector of identification, and the digital modulation signals are identified by using a probability neural network classifier. The digital modulation signal identification method has great identification performance on the digital modulation signals 2ASK, 4ASK, 2FSK, 4FSK and BPSK under standard distribution pulse noise.
Owner:XIDIAN UNIV
Soldered ball surface defect detection device and method based on machine vision
InactiveCN101424645AImprove classification abilityRealize non-destructive testingMaterial analysis by optical meansCollection systemImage segmentation
The invention relates to a device and a method based on machine vision for detecting the surface defects of solder balls in the field of automatic optical detection. An optical imaging system collects chip images, and an image collection system receives the collected chip images, intercepts a single-frame gray image from video stream signals output by the optical imaging system, and stores the single-frame gray image in the form of a two-dimensional integer matrix; an image segmentation module segments the whole two-dimensional integer matrix corresponding to the whole image into subsidiary matrices containing the solder balls, an image characteristic extraction module receives and processes the two-dimensional integer subsidiary matrices output by the mage segmentation module and outputs a one-dimensional floating point vector, and a probability neural network module receives, trains, and tests the one-dimensional floating point vector output by the image characteristic extraction module and divides the solder balls into two categories, namely good solder balls and defective solder balls. The device of the invention has simple structure, the graduation of a characteristic extraction method and a probability neural network is good, and the accuracy is high so as to realize the NDE (Non-Destructive Examination) of the surface defects of the solder balls.
Owner:SHANGHAI JIAO TONG UNIV
Infrared nondestructive testing method based on thermal image time sequence characteristics
InactiveCN103439342AOvercoming detectionOvercome limitationsOptically investigating flaws/contaminationPrincipal component analysisComputer science
The invention discloses an infrared nondestructive testing method based on thermal image time sequence characteristics. The new method with pixel as a unit is used for the infrared nondestructive test of flaws and is based on thermal image time sequence characteristics and PNN (probabilistic neural network). The infrared nondestructive testing method comprises the following steps: firstly, obtaining an infrared time sequence thermal image of a heating test piece in a cooling process by an infrared thermal imager; secondly, extracting the gray values of normal and abnormal areas in the time sequence thermal image, establishing the relationship between the gray values of different areas and time and then obtaining the corresponding initial time sequence characteristics; thirdly, reducing the dimensions of the initial characteristics by adopting main constituent analysis so as to obtain the time sequence characteristics; and finally establishing the probabilistic neural network with the time sequence characteristics as training samples.
Owner:EAST CHINA JIAOTONG UNIVERSITY
Hyper-spectral image technology-based detection method for distinguishing rice growing areas
ActiveCN104215584AImprove robustnessHigh accuracyColor/spectral properties measurementsWhiteboardFeature extraction
The invention discloses a hyper-spectral image technology-based detection method for distinguishing rice growing areas. The method comprises the following steps: (1) correcting black and white boards; (2) a training process: firstly, collecting a train sample image, correcting the sample image, extracting an interesting area, and extracting characteristics, wherein the characteristics comprise the percentage of chalky rice of rice samples of each growing area, the length-width ratio of each rice grain, and 15 texture eigenvalues, and building a prediction model by using a probabilistic neural network PNN method; and (3) testing to-be-detected rice samples. The detection method disclosed by the invention is fast and stable, and meanwhile, the classification accuracy is effectively improved.
Owner:SOUTH CHINA UNIV OF TECH
Cutter damage adaptive alarm method based on wavelet packet and probability neural network
InactiveCN102172849AHigh precisionTimely replacementMeasurement/indication equipmentsMaintainance and safety accessoriesProbit modelAcoustic emission
The invention discloses a cutter damage adaptive alarm method based on a wavelet packet and a probability neural network. The method comprises the following steps of: fixing an acoustic emission sensor on a cutter bar, acquiring acoustic emission signals, performing three-layer wavelet packet analysis, selecting characteristic frequency bands and taking root mean square values thereof, normalizing the root mean square values to obtain smoothing factors and prior probability, establishing a cutter damage state probability model by using a probability neural network, determining an alarm value of the cutter abrasion state according to the model and the Pauta criterion, forming a dynamic alarm line, and performing adaptive alarm monitoring of the cutter operating state according to the dynamic alarm line. By the method, the probability distribution curve of the root mean square value related with the cutter abrasion can be found, the alarm value is determined by using a mathematical statistic method, the dynamic alarm line is formed together with the cutter abrasion state change, and missing alarm and error alarm are not caused.
Owner:XI AN JIAOTONG UNIV
Wheel service state safety margin estimation and fault diagnosis method
InactiveCN106250613AScientific overhaulScientific maintenanceGeometric CADSubsonic/sonic/ultrasonic wave measurementFeature vectorFeature extraction
The invention discloses a wheel service state safety margin estimation and fault diagnosis method. The wheel service state safety margin estimation and fault diagnosis method comprises the following steps of firstly carrying out feature extraction for a steel rail vibration signal, decomposing the steel rail vibration signal by adopting an EMS (Empirical Mode Decomposition) method, calculating related characteristic indexes of each IMF (Intrinsic Mode Function) to serve as a characteristic vector of a wheel service state; secondly, classifying normal and fault states by utilizing LSSVM based on state characteristic vectors of steel rail vibration signals of a normal wheel and a faulted wheel, obtaining a safety margin of a train wheel and estimating the train wheel service state; and finally, carrying out fault mode identification for a normal wheel, a flat damage wheel and an out-of-round wheel by adopting a PNN (Probabilistic Neural Network), and providing reference basis for a vehicle maintenance department. The wheel service state safety margin estimation and fault diagnosis method has the advantages of high reliability and good engineering feasibility.
Owner:NANJING UNIV OF SCI & TECH
Excitation inrush current identification method based on wavelet transformation and probabilistic neural network (PNN)
InactiveCN102510044AAvoid Confusing JudgmentStrong discriminationEmergency protective circuit arrangementsBiological neural network modelsSignal onMATLAB
The invention relates to an excitation inrush current identification method based on wavelet transformation and a probabilistic neural network (PNN), in particular to a novel method for identifying excitation inrush current by combining wavelet analysis and the PNN. Data of the excitation inrush current and fault current are acquired by simulation by a model constructed in Matlab / Simulink; and energy characteristic values of fault signals on different frequency bands are extracted by using the wavelet transformation, and are used as input parameters of the PNN, so that the excitation inrush current and short circuit current can be identified. The method is high in identification capacity; and confused judgment on a real short circuit fault and the excitation inrush current is avoided.
Owner:SHANGHAI UNIVERSITY OF ELECTRIC POWER
Pattern recognition method based on self-adaptation correction neural network
ActiveCN103489009AImprove classification accuracyImplement batch mode fixesBiological neural network modelsCharacter and pattern recognitionPattern recognitionNerve network
The invention relates to the field of pattern recognition, in particular to a pattern recognition method based on a self-adaptation correction neural network. The method comprises the steps of classifying input training samples through a probabilistic neural network model so as to obtain samples accurate in classification and samples inaccurate in classification; adding an input layer, a central layer and an excitation layer on the basis of the probabilistic neural network model structure so as to construct a self-adaptation correction neural network model structure; for the samples inaccurate in classification in the probabilistic neural network model, using themself as central points, calculating the allowance radius between the the samples and samples of other classifications, clustering error samples of same category so as to realize batch correction of classification patterns and replanning of a judging interface and build the self-adaptation correction neural network; finally, conducting pattern recognition on input testing samples based on the self-adaptation correction neural network model. The pattern recognition method has the advantages of being high in accuracy in mode classification, strong in mode generalization ability, good in classification real-time performance, wide in application prospect, and the like.
Owner:NANJING NORTH OPTICAL ELECTRONICS
Probabilistic neural network-based tolerance-circuit fault diagnosis method
ActiveCN101819253AOvercome lossOvercoming structural complexityAnalog circuit testingBiological neural network modelsFault toleranceWavelet
Owner:HUNAN UNIV
Online safety detection prewarning device of elevator and detection prewarning method thereof
ActiveCN104925613AImprove reliabilityImprove accuracyElevatorsSelf adaptiveProbabilistic neural network
The invention relates to an online safety detection prewarning device of an elevator. The device comprises a car prewarning device and a machine room prewarning device, wherein the car prewarning device consists of a first sensing unit, a first acquiring unit, a first processing unit and a first near field communication unit; the machine room prewarning device consists of a second sensing unit, a second acquiring unit, a second processing unit and a second near field communication unit; the first near field communication unit and the second near field communication unit are in wireless communication; and the second processing unit is remotely communicated with a remote platform through a remote communication gateway. The invention further discloses an online safety detection prewarning method of the elevator. The device adopts a probability neural network algorithm; and the algorithm enables measured characteristic parameter thresholds to continuously self-learn, self-adapt and self-adjust along with such conditions as elevator ageing degree, maintenance condition and load magnitude, so that the wrong judgment or judgment missing probability is largely reduced, and the long-time use reliability and accuracy of a prewarning function of the device are improved.
Owner:安徽中科福瑞科技有限公司
Probabilistic neural network algorithm-based cable state evaluation method
ActiveCN106251059ARealize evaluationHigh precisionNeural architecturesResourcesAlgorithmAnalysis method
The invention discloses a probabilistic neural network algorithm-based cable state evaluation method. The method includes the following steps that: a multi-source information database is constructed according to many types of information sources such as basic information, operation information, operation inspection information and family defects of cable equipment; and the database under an incomplete information condition is pre-processed by using a factor analysis method, and a probabilistic neural network-based multi-source information algorithm is put forward, and the overall state of a cable under an incomplete information condition can be comprehensively evaluated. With the method of the invention adopted, the fault of a cable can be detected timely and judged accurately, and the operating state of the cable can comprehensively reflected.
Owner:CHINA ELECTRIC POWER RES INST +2
Intelligent diagnosis method for failure of wind generating set
InactiveCN107563069ARealize multiple fault diagnosis analysisSolve the problem that the fault diagnosis result cannot be obtained accuratelyCharacter and pattern recognitionNeural architecturesFault toleranceNODAL
The invention discloses an intelligent diagnosis method for the failure of a wind generating set. The diagnosis method includes the steps that first, according to historical failure knowledge information of the wind generating set, a failure tree model of the wind generating set is established; then according to the structure of the failure tree model, a probability neural network structure modelis established, and historical failure sample data of the wind generating set are mapped into failure mode space to form a failure diagnosis network model with high fault tolerance and adaptive ability; finally, the failure data is input into the established failure diagnosis network model to obtain a diagnosis result, a failure mode is output and then matched with a corresponding failure tree branch, leaf nodes governed by the failure tree branch are positioned, and namely reasons or reason combinations leading to the failure are found out. Based on a failure tree and a probability neural network, intelligent diagnosis is performed on the multi-form failure of the wind generating set, multi-failure diagnosis analysis is performed on complex failures of the wind generating set under incomplete information, and failure reasons are accurately positioned.
Owner:GUODIAN UNITED POWER TECH
Mobile terminal user identity authentication method based on multi-finger touch behavior characteristics
ActiveCN105068743AEasy to realize identificationImmutableDigital data authenticationInput/output processes for data processingFeature vectorSupport vector machine classifier
The present invention discloses a smart mobile terminal authentication method based on multi-finger touch behavior characteristics. The method comprises a first step of obtaining touch screen operating information of a user through multi-finger touch screen operation, the multi-finger touch screen operation being touch screen sliding of multiple fingers at a together-put state; a second step of extracting a physiological feature and a behavior feather of a touch screen from the user touch screen operation information, and combining the above feathers as one feature vector; a third step of training a probabilistic neural network classifier and a support vector machine classifier by using the above combined feather vector, to generate a training model of valid users; and a fourth step of collecting touch screen operation information of test users, processing the information according to the second step to obtain combined feature vectors of the touch screen operation of the test users, and putting the combined feature vectors into the two classifiers to compare with the training model of the valid users, thus to obtain classifying results if the test users are valid users.
Owner:XI AN JIAOTONG UNIV
Method applicable to identifying damage to space grid structure
ActiveCN103116759AReduce in quantityAvoiding Combinatorial Explosion ProblemsBiological neural network modelsCharacter and pattern recognitionNODALResearch Object
The invention relates to a method for identifying damage to a large structure, in particular to a method applicable to identifying damage to a space grid structure. The method includes performing substructure-oriented damage location for the integral structure as a research object, in other words, finely dividing the space grid structure into substructures according to a formation rule of the space grid structure, and identifying possibly damaged substructures by a probabilistic neural network; and performing node-oriented damage location for each integral substructure as a research object within a reduced range, locating damage to specific bars of integral nodes as research objects and determining damage degrees of the specific bars. The method has the advantages that the method is applicable to identifying damage to large structures such as bridges, power transmission towers and high-rise buildings, and is particularly applicable to identifying damage to long-span space grid structures with numerous nodes and bars, the structure of the neural network is simplified, the nonlinear mapping ability and the damage identifying efficiency are improved, and the method has certain engineering practical value.
Owner:江苏中闽重工科技有限公司
Probabilistic neural network based moving object detection method and an apparatus using the same
InactiveUS20150104062A1Accurate detectionImage enhancementImage analysisBackground imageVariable bit rate vbr
The present disclosure proposes a method of moving object detection in variable bit-rate video steams based on probabilistic neural networks, and the method features a background generation module and a moving object detection module. The background generation module produces a model of background images which express properties of variable bit-rate video streams. The moving object detection module distinguishes a moving object in both low and high bit-rate video steams in an efficient manner. The detection result is generated by calculating the output value of the probabilistic neural networks.
Owner:NAT TAIPEI UNIV OF TECH
Power transformer fault diagnosis method and system based on improved firefly algorithm optimization probabilistic neural network
InactiveCN110363277AImprove classification performanceFast searchCharacter and pattern recognitionArtificial lifeAlgorithmDiagnosis methods
The invention discloses a power transformer fault diagnosis method based on an improved firefly algorithm (PFA) optimized probabilistic neural network (PNN). The power transformer fault diagnosis method comprises the following steps: firstly, collecting fault characteristic gas by using a gas chromatographic analysis method and carrying out pretreatment by using a fused DGA algorithm; initializinga PNN neural network, a firefly algorithm and a two-dimensional particle swarm; taking the PNN smoothing factor as a firefly individual, and calculating the position and brightness of the firefly; feeding the solving result of each firefly algorithm back to the particle swarm algorithm, carrying out fitness evaluation on each particle, and updating the positions and speeds of the particles; carrying out loop iteration, substituting the obtained optimal smoothing factor into the PNN to carry out fault prediction, and training a PNN model after PFA optimization; inputting a test sample, and outputting a fault type result, thereby achieving the fault diagnosis of the power transformer. The method is high in search speed, high in diagnosis precision, small in error, and obvious in classification effect.
Owner:NANJING UNIV OF TECH
Identification of fire signatures for shipboard multi-criteria fire detection systems
A multi-criteria fire detection system, comprising a plurality of sensors, wherein each sensor is capable of detecting a signature characteristic of a presence of a fire and providing an output indicating the same. A processor for receiving each output of the plurality of sensors is also employed. The processor includes a probabilistic neural network for processing the sensor outputs. The probabilistic neural network comprises a nonlinear, nor-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter. The plurality of data sets includes a baseline, non-fire, fist data set; a second, fire data set, and a third, nuisance data set. The algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discrimination between said data sets, and whether the outputs suffice to substantially indicate the presence of a fire, as opposed to a non-fire or nuisance situation.
Owner:THE UNITED STATES OF AMERICA AS REPRESENTED BY THE SECRETARY OF THE NAVY
Fluvial facies reservoir step-by-step seismic facies prediction method based on geological information constraint
ActiveCN107356958APrediction is accurateFull forecastEarthquake measurementSeismologySeismic attributeResponse model
The invention discloses a fluvial facies reservoir step-by-step seismic facies prediction method based on geological information constraint. The method includes following steps: assessing geological data; establishing an analysis column chart, and comprehensively analyzing the column chart and a work area river channel sand body to obtain a superposition relation; abstracting a seismic response model; constructing a seismic sensitive attribute set; recognizing a seismic mode by employing a probabilistic neural network; performing related preprocessing operation on the seismic attribute; and performing seismic facies prediction to obtain a seismic facies map. The method is advantageous in that geological information is converted to monitoring information of the seismic scale and added to mode recognition of the seismic facies so that prediction results are more accurate, and clearer geological significance is achieved; the seismic facies prediction is performed by employing the probabilistic neural network so that clear indication significance is achieved for final seismic facies prediction results; the training time is greatly saved by employing the network training method; and the reservoir seismic facies in the range of the seismic scale can be fully predicted through the step-by-step prediction method without setting a classification number in advance.
Owner:SOUTHWEST PETROLEUM UNIV +1
Boiler combustion condition identification method based on information entropy characteristics and probability nerve network
ActiveCN103886405AAccurately reflectAvoid misleadingBiological neural network modelsResourcesNerve networkOxygen
The invention discloses a boiler combustion identification method based on information entropy characteristics and a probability nerve network. The method comprises steps of entering a data pretreatment procedure and obtaining typical load points and a characteristic sampling collection of corresponding exhaust smoke oxygen volume and furnace pressure signals through a data input interface, entering a sampling data entropy analysis process and calculating singular spectral entropy and power spectral entropy of the exhaust smoke oxygen volume and furnace pressure signals under the corresponding working condition, using the obtained entropy value signals and the corresponding load working condition point as a training data collection to construct a PNN boiler combustion working condition identification model and outputting the result to a client terminal to join the optimization operation guide and the condition detection. The invention not only solves procedure state characterization problem in the furnace but also reflects the attributes of the furnace operation performance timely and accurately, avoids fault guidance for the operation personnel caused by falsity data and wrong data, and provides a reference model to the boiler operation optimization, state monitor and failure diagnosis of a power plant monitor information system.
Owner:SOUTHEAST UNIV +1
Visible light channel joint balance method based on orthogonal mapping and probabilistic neural network
ActiveCN107124378ASuppress interferenceHigh data transmission reliabilityClose-range type systemsRadio transmissionEngineeringData transmission
The invention discloses a visible light channel joint balance method based on orthogonal mapping and a probabilistic neural network. The method comprises a transmitting end and a receiving end. Signals are transmitted from the transmitting end to the receiving end through visible light MIMO (Multiple Input Multiple Output) channels. The visible light MIMO channels are multiple input multiple output channels. According to joint balance, preposition balance and postposition balance are combined. According to the method, a joint balance scheme combing a preposition balance technology and a postposition balance technology is employed, namely, the visible light multiple input multiple output channel joint balance method based on the orthogonal mapping and the probabilistic neural network is employed. The interference between the channels of a visible light MIMO communication system can be effectively suppressed and the data transmission reliability is improved.
Owner:SUN YAT SEN UNIV
Fault diagnosis method for flow passage of steam turbine
ActiveCN104142254AAccurate diagnosisStructural/machines measurementIndustrial monitoringFeature extraction
The invention belongs to the field of industrial monitoring, particularly relates to the application of an empirical mode decomposition method and a probabilistic neural network in the aspect of fault diagnosis of flow passages of heat-engine plant steam turbines, and provides a fault diagnosis method for a flow passage of a steam turbine on the basis of empirical mode decomposition and the probabilistic neural network. The method includes the steps that firstly, fault symptom parameter data of the flow passage are collected when the heat-engine plant steam turbine operates normally and has different faults, fault features are extracted from the corresponding symptom parameter data by the utilization of the advantages of empirical mode decomposition in processing non-stationary and nonlinear data, fault detection and recognition are carried out by the utilization of the powerful nonlinear mode classification performance of the probabilistic neural network, and then faults of the flow passage of the steam turbine are effectively diagnosed. By means of the fault diagnosis method, the faults of the flow passage of the heat-engine plant steam turbine can be diagnosed rapidly and accurately.
Owner:ZHEJIANG UNIV
Photovoltaic array fault diagnosis method and device based on spatiotemporal distribution characteristics
ActiveCN109842373AEffectively describe the distribution lawImprove diagnostic accuracyPhotovoltaic monitoringPhotovoltaic energy generationData setSpatial function
The invention relates to a photovoltaic array fault diagnosis method and device based on spatiotemporal distribution characteristics. A photovoltaic output time component and a spatial component are calculated and obtained by a time and spatial distribution function for the photovoltaic array output starting from the historical operation data and the historical environment data of a photovoltaic power plant, the components are used as a training data set, a probabilistic neural network is trained by the training data set, a photovoltaic array fault diagnosis model is obtained, a time functionand a spatial function for photovoltaic branch output are established to effectively describe the distribution law of string current under different fault conditions, the photovoltaic power station DCside array fault diagnosis method established based on the probabilistic neural network is high in diagnosis precision, and finally, branch current data are inputted to the photovoltaic array fault diagnosis model to realize diagnosis of multiple types of fault, and the existing data of the actual project can be fully utilized effectively.
Owner:STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST +2
Rapid unit failure diagnosis method based on full state information
ActiveCN103558042AFast trainingReduce the requirement for own experienceStructural/machines measurementFeature vectorDiagnostic data
The invention discloses a rapid unit failure diagnosis method based on full state information. The method includes the steps that a probabilistic neural network is used for conducting unit failure recognition; 14 types of typical failure data are used for obtaining the characteristic values of all the 14 types of failure through holographic spectral analysis or time domain statistic analysis, and all the characteristic values constitute characteristic vectors; the 14 types of sample characteristic vectors are used as the weight vectors Wj of 14 types of mode units respectively; the data to be diagnosed are selected to conduct the holographic spectral analysis so that the characteristic vectors can be obtained to be used as the input vectors Xj of a neural network input layer, and input sample data and training sample data adopt the same parameter; scalar product calculation is carried out on the input vector Xj and the weight vector Wj of each mode unit; summation of the outputs g(Zj) of the mode units corresponding to the same failure mode is conducted so that the probability density of the failure can be estimated; the outputs fR(X) of 14 accumulation layers corresponding to the 14 types of failure modes are used as inputs so that the failure modes can be judged through the Bayes judgment strategy. By means of the rapid unit failure diagnosis method based on the full state information, the expertise in the field can be fully utilized, and the requirement for the experience of a user himself or herself is lowered.
Owner:CHINA PETROLEUM & CHEM CORP
Bearing health state identification method based on probabilistic neural network
InactiveCN103512751ASlow down or stop the diseaseExtended service lifeMachine bearings testingBiological neural network modelsFeature setAssessment data
A bearing health state identification method based on the probabilistic neural network comprises the first step of collecting original signals; the second step of selecting and evaluating parameters, wherein the capability for allocating known data of the selected parameters is evaluated according to a feature set and the selected statistic features, and the feature set is generated by a vibration signal worked out based on the principle of statistics; the third step of constructing a probabilistic neural network model; the fourth step of inputting the input parameters to the probabilistic neural network to carry out state identification. According to the method, the operating states of a bearing comprise a normal state, a sub-health state and a failure state, the health states of the bearing are evaluated based on the PNN, a sample entropy and the like are used for serving as the input feature parameters of the PNN, the distribution capability of the data is evaluated, and the recognized result of the bearing health states is obtained by experimentally comparing a traditional normal-failure model of the bearing states.
Owner:LIAONING UNIVERSITY
Self-adapt dynamic apparatus status alarming method based on probability model
InactiveCN1472674AClear guidanceAvoid false positivesComplex mathematical operationsDynamic dataComputer science
The method uses self-learning mode to construct equipment state probability model with probability nervous network based on dynamic data of equipment operation. The model adjusts its probability distribution by following to the equipment operation to describe variation rule of the equipment state dynamically. Relying on the model, the dynamic evaluation rule of the equipment state is studied and simultaneously the threshold for different state is set up to form adaptive alarming limit for the equipment operation. The equipment state can be graded in three classes of normal, fault and quick deterioration by utilizing this adaptive alarming method.
Owner:XI AN JIAOTONG UNIV
Signal identification method of fiber perimeter early-warning system of airport
InactiveCN105023379AEfficient identificationTaking into account real-time requirementsBurglar alarmFiberEngineering
The invention relates to a signal identification method of a fiber perimeter early-warning system of an airport. The method comprises the following steps: (1) signal acquisition; to be specific, collecting a light signal by a perimeter early-warning system and converting the signal into an original electric signal X(n); (2), pretreatment; to be specific, carrying out processing like filtering and amplification on the original electric signal X(n) to obtain an electric signal X' (n); (3), downsampling; to be specific, carrying out downsampling on a disturbing signal to obtain an x(n); (4), time-frequency characteristic obtaining at a zero level; to be specific, carrying out processing on the signal x(n) after downsampling according to a formula to obtain a time-frequency characteristic; (5) characteristic extraction; to be specific, extracting a maximum value M, a zero-crossing frequency number K, frequency deviation D, a frequency sample entropy S, and a total signal energy amount E; and (6), intrusion classification; to be specific, inputting five typical characteristics into probabilistic neural networks of five input layers and determining an intrusion type based on comparison of output layers. With the method, a problem that the signal identification precision are affected by the non-stable characteristic of the output signal and the similarity of the intrusion signal and the false-alarm signal of the fiber perimeter early-warning system of the airport can be solved; different disturbance types can be identified effectively; and the real-time performance and practicability are high.
Owner:CIVIL AVIATION UNIV OF CHINA
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