Patents by Inventor Liulu HE

Liulu HE has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210224635
    Abstract: A transformer failure diagnosis method and system based on an integrated deep belief network are provided. The disclosure relates to the fields of electronic circuit engineering and computer vision. The method includes the following: obtaining a plurality of vibration signals of transformers of various types exhibiting different failure types, retrieving a feature of each of the vibration signals, and establishing training data through the retrieved features; training a plurality of deep belief networks exhibiting different learning rates through the training data and obtaining a failure diagnosis correct rate of each of the deep belief networks; and keeping target deep belief networks corresponding to the failure diagnosis correct rates that satisfy requirements, building an integrated deep belief network through each of the target deep belief networks, and performing a failure diagnosis on the transformers through the integrated deep belief network.
    Type: Application
    Filed: December 18, 2020
    Publication date: July 22, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Chaolong ZHANG, Guolong SHI, Hui ZHANG, Liulu HE, Bolun DU
  • Publication number: 20210190882
    Abstract: A transformer failure identification and location diagnosis method based on a multi-stage transfer learning theory is provided. Simulation is set up first, a winding parameter of a transformer to be tested is calculated, and a winding equivalent circuit is accordingly built. Different failures are configured for the equivalent circuit, and simulation is performed to obtain a large number of sample data sets. A sweep frequency response test is performed on the transformer to be tested, and detection data sets are obtained. Initial network training is performed on simulation data sets by using the transfer learning method, and the detection data sets are further trained accordingly. A failure support matrix obtained through diagnosis is finally fused. The multi-stage transfer learning theory is provided by the disclosure.
    Type: Application
    Filed: November 26, 2020
    Publication date: June 24, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Jiajun DUAN, Xiaoxin WU, Liulu HE, Hui ZHANG
  • Publication number: 20210175986
    Abstract: A wireless channel monitoring and simulation device with multi-input multi-output (MIMO) is provided, which includes: a wireless channel monitor, configured to collect characteristic parameters of wireless channels in typical environments, and establish models based on the characteristic parameters; a model database, configured to store the characteristic parameter models and parameterize; an original signal, configured to input N different original signals; a wireless channel simulator, configured to simulate a typical channel environment according to the model database configuration, so that the original signal is the same as that in a real typical channel environment, and adopts a N-path output; an N-channel oscilloscope, configured to observe specific waveforms of N-path simulated signals; and a master computer software, configured to process, analyze, and store N-path output signals.
    Type: Application
    Filed: October 28, 2020
    Publication date: June 10, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Luqiang Shi, Guolong SHI, Shuiqing Xu, Liulu HE, Yuting Wu
  • Publication number: 20210176093
    Abstract: A channel prediction system and a channel prediction method for an OFDM wireless communication system include a standard echo state network and a two-layer adaptive elastic network. In the method, with respect to each subcarrier of a pilot OFDM symbol, an echo state network is trained by using frequency domain channel information of each subcarrier obtained by channel estimation. The trained echo state network may realize short-term prediction of the frequency domain channel information. To overcome a likely ill-conditioned solution of an output weight in an echo state network, the output weight in the echo state network is estimated by using a two-layer adaptive elastic network.
    Type: Application
    Filed: November 15, 2020
    Publication date: June 10, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Yongbo SUI, Guolong SHI, Liulu HE, Hui ZHANG, Chaolong ZHANG
  • Publication number: 20210150161
    Abstract: A method for vehicle-loading warehousing asset management based on an ultra-high frequency (UHF) radio frequency identification (RFID) path loss model, which includes the following steps. An electromagnetic wave is emitted by a tag reader. The electromagnetic wave is diffracted, reflected, and scattered when passing through a warehousing vehicle hood, and the electromagnetic wave is emitted and scattered through the ground. A UHF RFID tag attached to a front surface location region of assets receives electromagnetic waves of various paths emitted by the tag reader. The tag reader reads UHF RFID tag information. A transfer function of a tag receiving signal is constructed according to the tag information, and a path loss function during a UHF RFID tag sensing electromagnetic wave process is constructed according to the transfer function. The path loss is calculated according to the constructed path loss function. A location of the UHF RFID tag is obtained.
    Type: Application
    Filed: October 8, 2020
    Publication date: May 20, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Guolong SHI, Liulu HE
  • Patent number: 11002789
    Abstract: An analog circuit fault feature extraction method based on a parameter random distribution neighbor embedding winner-take-all method, comprising the following steps: (1) collecting a time-domain response signal of an analog circuit under test, wherein the input of the analog circuit under test is excited by using a pulse signal, a voltage signal is sampled at an output end, and the collected time-domain response signal is an output voltage signal of the analog circuit; (2) applying a discrete wavelet packet transform for the collected time-domain response signal to acquire each wavelet node signal; (3) calculating energy values and kurtosis values of the acquired wavelet node signals to form an initial fault feature data set of the analog circuit; and (4) analyzing the initial fault feature data by the parameter random distribution neighbor embedding winner-take-all method, to acquire optimum low-dimensional feature data.
    Type: Grant
    Filed: October 20, 2018
    Date of Patent: May 11, 2021
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Wei He, Hui Zhang, Liulu He, Baiqiang Yin, Bing Li
  • Publication number: 20210117770
    Abstract: A power electronic circuit troubleshoot method based on a beetle antennae optimized deep belief network algorithm including the following steps is provided. Output current signals of DC bus of a three-phase PWM rectifier under different switching device open circuit failure modes are collected as an original data set. Intrinsic mode function components of the output current signals under different switching device open circuit failure modes are extracted using empirical mode decomposition to construct an original failure feature set. Fault feature is selected based on extra-trees to generate final fault dataset. A structure of a deep belief network is optimized using a beetle antennae algorithm. An optimized deep belief network is trained using a training set and an obtained failure recognition result is verified using a testing set.
    Type: Application
    Filed: May 14, 2020
    Publication date: April 22, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Yaru ZHANG, Liulu HE
  • Publication number: 20210089900
    Abstract: The disclosure discloses a transformer DGA data prediction method based on multi-dimensional time sequence frame convolution LSTM, including the steps: firstly, collecting and dividing monitoring information of dissolved gas in transformer substation oil into a test set and a verification set; secondly, extracting characteristic parameters by adopting a non-coding ratio method, deleting data which are basically kept unchanged, and performing normalization, noise superposition etc.
    Type: Application
    Filed: September 15, 2020
    Publication date: March 25, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Jiajun DUAN, Liulu HE, Wenjie WU
  • Publication number: 20210048487
    Abstract: The disclosure discloses a power transformer winding fault positioning method based on deep convolutional neural network integrated with visual identification, including 1) a winding equivalent circuit is established, and a transfer function thereof is calculated; 2) a sine wave excitation source is set at one end of the power transformer winding to obtain the amplitude-frequency characteristic curve of each winding node; 3) circuits under various fault statuses are subjected to scanning frequency response analysis to extract amplitude-frequency characteristics; 4) a feature matrix is established based on the obtained amplitude-frequency characteristics; 5) scanning frequency response analysis is performed on the diagnosed power transformer to form a feature matrix; 6) the feature matrix is converted into an image, simulation and historical detection data are used as a training set, and a deep convolutional neural network is input for training; 7) diagnosed transformer is subjected to fault classification and
    Type: Application
    Filed: December 30, 2019
    Publication date: February 18, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Jiajun DUAN, Bolun DU, Hui ZHANG, Liulu HE
  • Publication number: 20210036503
    Abstract: An intermediate relay maloperation preventing device and method based on an improved recursive wavelet algorithm is provided. The device includes a power supply module, a voltage sampling circuit, an analog-to-digital conversion module, a DSP chip, and a relay maloperation signal shielding module. The voltage sampling circuit is connected to the analog-to-digital conversion module. The analog-to-digital conversion module is connected to the DSP chip. The DSP chip is connected to and controls a relay signal control module. The voltage sampling circuit collects a voltage. An improved recursive wavelet is used to extract a voltage feature. As such, identification of a fault signal and a normal signal is achieved, and real-time fault monitoring is accomplished. The detection method may be easily implemented, exhibits good filtering performance and anti-interference capability, delivers high detection accuracy, and may accomplish real-time online monitoring of intermediate relay faults.
    Type: Application
    Filed: July 31, 2020
    Publication date: February 4, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Jianbo ZHOU, Hui ZHANG, Liulu HE, Weibo YUAN, Yi RUAN, Bing LI
  • Publication number: 20210020360
    Abstract: The disclosure provides an internal thermal fault diagnosing method for an oil-immersed transformer based on DCNN and image segmentation, including: 1) dividing an internal area of a transformer, and using fault areas and normal status as labels of DCNN; 2) through lattice Boltzmann simulation, randomly obtaining multiple feature images of the internal temperature field distribution of the transformer under normal and various fault state modes, and the fault area serves as a label to form the underlying training sample set; 3) obtaining historical monitoring information of the infrared camera or temperature sensor, and forming its corresponding fault diagnosis results into labels; 4) combining all monitoring information contained in each sample into one image, and then extracting the same monitoring information from the samples in the sample set to form a new image; 5) segmenting image sample and then inputting the same into DCNN for training to obtain diagnosis results.
    Type: Application
    Filed: January 13, 2020
    Publication date: January 21, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Jiajun DUAN, Liulu HE
  • Publication number: 20210003640
    Abstract: The disclosure discloses a fault locating method based on a multi-layer evaluation model. Firstly, determine a fault type to be inspected and a fault symptom which able to accurately and effectively reflect a power transformer operation status and determine a weight of each fault type by using an association rule and a set pair analysis. Then, establish a DBN model to perform feature extraction and classification on multi-dimensional data of a fault. Finally, perform a comprehensive evaluation on an existing diagnosis result by using the D-S evidence theory. Accordingly, the supporting strength of the common target is reinforced, while the influence of divergent targets is reduced. As a result, the uncertainty in the diagnosis result is significantly reduced. The disclosure is mainly used to monitor and diagnose a status variable of the power transformer in a real-time manner, and treats power transformer status evaluation as a multi-property decision issue.
    Type: Application
    Filed: November 20, 2019
    Publication date: January 7, 2021
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Wenjie WU, Hui ZHANG, Liulu HE
  • Patent number: 10877084
    Abstract: A nonlinear model transformation solving and optimization method for partial discharge positioning based on multi-ultrasonic sensor includes the following steps: (1) constructing a spatial rectangular coordinate system in a transformer, and setting a position of each ultrasonic sensor; (2) constructing a positioning model on the basis of an arrival time positioning method to obtain a nonlinear positioning equation set for solving a position of a PD source; (3) eliminating second-order terms in the nonlinear positioning equation set to transform the nonlinear positioning equation set into a linear equation set; (4) obtaining multiple sample initial values of a coordinate of the PD source; (5) screening the multiple sample initial values; (6) performing clustering processing on the multiple effective sample initial values by adopting an improved K-means algorithm; and (7) selecting a class with most cluster elements, and calculating a mean of the elements of the class to finally determine an optimal coordinate
    Type: Grant
    Filed: August 22, 2018
    Date of Patent: December 29, 2020
    Assignee: WUHAN UNIVERSITY
    Inventors: Baiqiang Yin, Yigang He, Hui Zhang, Bing Li, Liulu He
  • Publication number: 20200403610
    Abstract: A serial IGBT voltage equalization method and system based on an auxiliary voltage source is disclosed. The method includes the following steps. (1) Detect a port dynamic voltage of each serial IGBT. (2) Perform dynamic overvoltage diagnosis respectively on the port dynamic voltage of each IGBT. (3) Supply emergency high level signal to the gate of the IGBT when there is dynamic overvoltage. (4) Stop supplying emergency high level signal to the gate of the IGBT, supply a constant voltage at the gate of the IGBT through the auxiliary voltage source. The invention provides a constant voltage through the auxiliary voltage source, prolongs the off time of the faulty IGBT, and turns off other IGBTs simultaneously, thereby achieving the purpose of serial IGBT voltage equalization.
    Type: Application
    Filed: December 11, 2019
    Publication date: December 24, 2020
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Lie LI, Liulu HE, Chenyuan WANG
  • Publication number: 20200394354
    Abstract: A method for diagnosing analog circuit fault based on cross wavelet features includes steps of: inputting an excitation signal to an analog circuit under test, and collecting time domain response output signals to form an original data sample set; dividing the original data sample set into a training sample set and a test sample set; performing cross wavelet decomposition on both sets; applying bidirectional two-dimensional linear discriminant analysis to process the wavelet cross spectra of the training sample set and the test sample set, and extracting fault feature vectors of the training sample set and the test sample set; submitting the fault feature vectors of the training sample set to a support vector machine for training an SVM classifier, constructing a support vector machine fault diagnosis model; and inputting the fault feature vectors of the test sample set into the model to perform fault classification.
    Type: Application
    Filed: December 7, 2017
    Publication date: December 17, 2020
    Inventors: Yigang He, Wei He, Zhigang Li, Lei Zuo, Bing Li, Liulu He
  • Publication number: 20200386811
    Abstract: A power electronic circuit fault diagnosis method based on Extremely randomized trees (ET) and Stack Sparse auto-encoder (SSAE) algorithm includes the following. First, collect the fault signal and extract fault features. Then, reduce the dimensionality of fault features by calculating the importance value of all features using ET algorithm. A proportion of the features to be eliminated is determined, and a new feature set is obtained according the value of importance. Further extraction of fault features is carried by using SSAE algorithm, and hidden layer features of the last sparse auto-encoder are obtained as fault features after dimensionality reduction. Finally, the fault samples in a training set and a test set are input to the classifier for training to obtain a trained classifier. And mode identification, wherein the fault of the power electronic circuit is identified and located by the training classifier.
    Type: Application
    Filed: November 12, 2019
    Publication date: December 10, 2020
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Yaru ZHANG, Liulu HE
  • Publication number: 20200387785
    Abstract: A method includes steps: 1) obtaining monitoring information of different monitoring points in normal state of power equipment; 2) setting faults and obtaining monitoring information of different fault types, positions, monitoring points of the equipment; 3) taking the monitoring information obtained in steps 1) to 2) as training dataset, taking the fault types and positions as labels, inputting the training dataset and the labels to deep CNN for training; 4) collecting monitoring data, performing verification and classification using step 3), obtaining probability values corresponding to each of the labels; 5) taking classification results of different labels as basic probability assignment values, with respect to a monitoring system composed of multiple sensors, taking different sensors as different evidences for decision fusion, performing fusion processing using the DS evidence theory to obtain fault diagnosis result.
    Type: Application
    Filed: December 24, 2019
    Publication date: December 10, 2020
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Jiajun DUAN, Hui ZHANG, Liulu HE
  • Publication number: 20200313612
    Abstract: The disclosure provides a silicon photovoltaic cell scanning eddy current thermography detection platform and a defect classification method. The technical solution adopted by the disclosure is: firstly, fixing the position of the electromagnetic inductive coil and the thermal imager, and using the main conveyor belt to carry the silicon photovoltaic cell to move forward on the production line to form a scanning eddy current heating of the silicon photovoltaic cell. Secondly, the defect temperature information is obtained through the thermal imager in terms of thermal image sequences. Thirdly, the feature extraction algorithms are used to extract the silicon photovoltaic cell defect features. Finally, the image classification algorithms are used to classify the silicon photovoltaic cell defects, and the sorting conveyor belts are used to realize the automatic sorting of silicon photovoltaic cells with different types of defects on the production line.
    Type: Application
    Filed: October 4, 2019
    Publication date: October 1, 2020
    Applicant: WUHAN UNIVERSITY
    Inventors: Yigang HE, Bolun DU, Yaru ZHANG, Jiajun DUAN, Liulu HE
  • Publication number: 20200300907
    Abstract: An analog-circuit fault diagnosis method based on continuous wavelet analysis and an ELM network comprises: data acquisition: performing data sampling on output responses of an analog circuit respectively through Multisim simulation to obtain an output response data set; feature extraction: performing continuous wavelet analysis by taking the output response data set of the circuit as training and testing data sets respectively to obtain a wavelet time-frequency coefficient matrix, dividing the coefficient matrix into eight sub-matrixes of the same size, and performing singular value decomposition on the sub-matrixes to calculate a Tsallis entropy for each sub-matrix to form feature vectors of corresponding faults; and fault classification: submitting the feature vector of each sample to the ELM network to implement accurate and quick fault classification.
    Type: Application
    Filed: January 6, 2017
    Publication date: September 24, 2020
    Applicant: HEFEI UNIVERSITY OF TECHNOLOGY
    Inventors: Yigang HE, Wei HE, Qiwu LUO, Zhigang LI, Tiancheng SHI, Tao WANG, Zhijie YUAN, Deqin ZHAO, Luqiang SHI, Liulu HE
  • Patent number: 10776232
    Abstract: A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.
    Type: Grant
    Filed: July 4, 2018
    Date of Patent: September 15, 2020
    Assignee: WUHAN UNIVERSITY
    Inventors: Yigang He, Chaolong Zhang, Hui Zhang, Baiqiang Yin, Jinguang Jiang, Liulu He, Jiajun Duan