Patents by Inventor Zhongkui ZHU
Zhongkui ZHU 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).
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Patent number: 11886311Abstract: The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.Type: GrantFiled: August 4, 2020Date of Patent: January 30, 2024Assignee: SOOCHOW UNIVERSITYInventors: Changqing Shen, Xu Wang, Jing Xie, Aiwen Zhang, Dong Wang, Xiaofeng Shang, Dongmiao Song, Xingxing Jiang, Jun Wang, Juanjuan Shi, Weiguo Huang, Zhongkui Zhu
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Publication number: 20230168150Abstract: The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training theType: ApplicationFiled: January 13, 2021Publication date: June 1, 2023Inventors: Changqing SHEN, Shuangjie LIU, Xu WANG, Dong WANG, Yongjun SHEN, Zaigang CHEN, Aiwen ZHANG, Xingxing JIANG, Juanjuan SHI, Weiguo HUANG, Jun WANG, Guifu DU, Zhongkui ZHU
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Patent number: 11644383Abstract: The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.Type: GrantFiled: November 26, 2020Date of Patent: May 9, 2023Assignee: SOOCHOW UNIVERSITYInventors: Changqing Shen, Yu Xia, Lin Kong, Liang Chen, Piao Lei, Yongjun Shen, Dong Wang, Hongbo Que, Aiwen Zhang, Minjie Chen, Chuancang Ding, Xingxing Jiang, Jun Wang, Juanjuan Shi, Weiguo Huang, Zhongkui Zhu
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Patent number: 11644391Abstract: The present invention discloses a fault diagnosis method under a convergence trend of a center frequency, including: (1) acquiring a dynamic signal x(t) of a rotary machine equipment; (2) setting initial decomposition parameters of a variational model; (3) decomposing the dynamic signal x(t) by using the variational model with the set initial decomposition parameters, and traversing a signal analysis band and performing iterative decomposition on the dynamic signal x(t) under the guidance of a convergence trend of a center frequency, to obtain optimized modals {m1 . . . mn . . . mN} and corresponding center frequencies {?1 . . . ?n . . . ?N}; (4) searching a fault related modal mI, guiding parameter optimization by using a center frequency ?I of the fault related modal mI, and retrieving an optimal target component mI including fault information; and (5) performing envelopment analysis on the optimal target component mI, and diagnosing the rotary machine equipment according to an envelope spectrum.Type: GrantFiled: July 30, 2020Date of Patent: May 9, 2023Assignee: SOOCHOW UNIVERSITYInventors: Xingxing Jiang, Changqing Shen, Jianqin Zhou, Dongmiao Song, Wenjun Guo, Guifu Du, Jun Wang, Juanjuan Shi, Weiguo Huang, Zhongkui Zhu
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Publication number: 20220373430Abstract: The invention provides an adaptive manifold probability distribution-based bearing fault diagnosis method, including constructing transferable domains and transfer tasks; converting a data sample in each transfer task into frequency domain data via Fourier transform, inputting the frequency domain data into a GFK algorithm model, and calculating a manifold feature representation matrix related to a bearing fault in each transfer task by using the GFK algorithm model; calculating a cosine distance between centers of a target domain and a source domain in each transfer task according to a manifold feature representation, and defining a target function of in-domain classifier learning; then solving the target function, to obtain a probability distribution matrix of the target domain; and selecting a label corresponding to the largest probability value corresponding to each data sample in the target domain from the probability distribution matrix as a predicted label of the data sample in the target domain.Type: ApplicationFiled: November 26, 2020Publication date: November 24, 2022Inventors: Changqing SHEN, Yu XIA, Lin KONG, Liang CHEN, Piao LEI, Yongjun SHEN, Dong WANG, Hongbo QUE, Aiwen ZHANG, Minjie CHEN, Chuancang DING, Xingxing JIANG, Jun WANG, Juanjuan SHI, Weiguo HUANG, Zhongkui ZHU
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Publication number: 20220327035Abstract: The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.Type: ApplicationFiled: August 4, 2020Publication date: October 13, 2022Inventors: Changqing SHEN, Xu WANG, Jing XIE, Aiwen ZHANG, Dong WANG, Xiaofeng SHANG, Dongmiao SONG, Xingxing JIANG, Jun WANG, Juanjuan SHI, Weiguo HUANG, Zhongkui ZHU
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Publication number: 20220050024Abstract: The present invention discloses a fault diagnosis method under a convergence trend of a center frequency, including: (1) acquiring a dynamic signal x(t) of a rotary machine equipment; (2) setting initial decomposition parameters of a variational model; (3) decomposing the dynamic signal x(t) by using the variational model with the set initial decomposition parameters, and traversing a signal analysis band and performing iterative decomposition on the dynamic signal x(t) under the guidance of a convergence trend of a center frequency, to obtain optimized modals {m1 . . . mn . . . mN} and corresponding center frequencies {?1 . . . ?n . . . ?N}; (4) searching a fault related modal mI, guiding parameter optimization by using a center frequency ?I of the fault related modal mI, and retrieving an optimal target component mI including fault information; and (5) performing envelopment analysis on the optimal target component mI, and diagnosing the rotary machine equipment according to an envelope spectrum.Type: ApplicationFiled: July 30, 2020Publication date: February 17, 2022Inventors: Xingxing JIANG, Changqing SHEN, Jianqin ZHOU, Dongmiao SONG, Wenjun GUO, Guifu DU, Jun WANG, Juanjuan SHI, Weiguo HUANG, Zhongkui ZHU
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Publication number: 20210374506Abstract: The present invention discloses a method for predicting bearing life based on a hidden Markov model (HMM) and transfer learning, including the following steps: (1) acquiring an original signal of full life of a rolling bearing; and extracting a feature set including a time domain feature, a time-frequency domain feature, and a trigonometric function feature; (2) inputting the feature set into an HMM to predict a hidden state, to obtain a failure occurrence time (FOT); (3) constructing a multilayer perceptron (MLP) model, obtaining a domain invariant feature and an optimal model parameter, and obtaining a neural network life prediction model; and (4) inputting the remaining target domain feature sets into the neural network life prediction model, and predicting the remaining life of the bearing. In the present invention, MLP-based transfer learning is used to resolve distribution differences in a source domain and a target domain caused by different operating conditions.Type: ApplicationFiled: August 7, 2020Publication date: December 2, 2021Inventors: Jun ZHU, Changqing SHEN, Nan CHEN, Dongmiao SONG, Jianqin ZHOU, Jun WANG, Juanjuan SHI, Weiguo HUANG, Zhongkui ZHU