公开(公告)号：US20230023931A1

公开(公告)日：2023-01-26

申请号：US17859244

申请日：2022-07-07

申请人： Zhejiang University

发明人： Zheming Tong ,  Jiage Xin ,  Shuiguang Tong

IPC分类号： F03B11/00

摘要： The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged. The present invention can effectively identify the occurrence of incipient cavitation in the hydraulic turbine set, reducing unnecessary shutdown of the equipment and prolonging the service life.

公开(公告)号：US11840998B2

公开(公告)日：2023-12-12

申请号：US17859244

申请日：2022-07-07

申请人： Zhejiang University

发明人： Zheming Tong ,  Jiage Xin ,  Shuiguang Tong

IPC分类号： F03B11/00 ,  G06F18/2137 ,  G06F18/243

CPC分类号： F03B11/008 ,  F05B2260/80 ,  F05B2270/305 ,  F05B2270/333 ,  F05B2270/404 ,  F05B2270/709 ,  F05B2270/80 ,  G06F18/2137 ,  G06F18/24323

摘要： The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged. The present invention can effectively identify the occurrence of incipient cavitation in the hydraulic turbine set, reducing unnecessary shutdown of the equipment and prolonging the service life.

公开(公告)号：US11816762B2

公开(公告)日：2023-11-14

申请号：US16939054

申请日：2020-07-26

申请人： Zhejiang University

发明人： Feiyun Cong ,  Huimin Li ,  Shuiguang Tong

IPC分类号： G06T11/00 ,  G06T17/20

CPC分类号： G06T11/005 ,  G06T17/20

摘要： A three-dimensional reconstruction method based on half-peak probability density distribution, including: slicing three-dimensional point cloud along Z-axis direction to obtain N spatial layers; extracting the scatter information in i-th spatial layer and projecting information to Zi plane; constructing membership function of each grid and scatter in the Zi plane and drawing a three-dimensional probability density plot; making a plane parallel to XOY plane through half-peak wmax/2 of three-dimensional probability density plot, parallel intersecting a three-dimensional probability density plot to obtain a contour LXY; superimposing radioactive source reconstruction contours corresponding to N spatial layers sequentially to obtain a three-dimensional reconstruction model of a radioactive source. The method can be applied to the nuclear electrical field, to achieve the rapid reconstruction of radioactive sources after nuclear accidents and accurate contour reconstruction after decommissioning of radioactive sources.

公开(公告)号：US11775704B2

公开(公告)日：2023-10-03

申请号：US18073990

申请日：2022-12-02

申请人： Zhejiang University ,  Xizi Clean Energy Equipment Manufacturing Co., ltd.

发明人： Shuiguang Tong ,  Zheming Tong ,  Jianyun Zhao ,  Weixiao He ,  Haidan Wang ,  Wei Chen

IPC分类号： G06F30/17 ,  G06F30/27

CPC分类号： G06F30/17 ,  G06F30/27

摘要： The present invention discloses an optimization design method for structural parameters of biomass boiler economizers and belongs to the field of big data learning models. In the present invention, a sample database is established by utilizing historical operating big data of biomass boiler economizers, a heat exchanger residual self-attention convolution model is established based on a CNN and a self-attention mechanism, a plurality of target parameters to be optimized are quickly predicted through machine learning, and multi-target optimization of structural parameters to be optimized in the economizers can be performed in combination with an iterative optimization algorithm. Compared with traditional optimization for all variables of a biomass boiler economizer, the self-attention mechanism can automatically focus on features with high importance, to better optimize variables with high importance, making the subsequent optimization and adjustment convenient and quick, and greatly reducing the optimization cost.

公开(公告)号：US20200380738A1

公开(公告)日：2020-12-03

申请号：US16939054

申请日：2020-07-26

申请人： Zhejiang University

发明人： Feiyun Cong ,  Huimin Li ,  Shuiguang Tong

IPC分类号： G06T11/00 ,  G06T17/20

摘要： The present invention discloses a three-dimensional reconstruction method based on half-peak probability density distribution, comprising the following steps: slicing three-dimensional point cloud along Z-axis direction to obtain N spatial layers; extracting the scatter information in i-th spatial layer and projecting information to Zi plane; constructing membership function of each grid and scatter in the Zi plane and drawing a three-dimensional probability density plot; making a plane parallel to XOY plane through half-peak wmax/2 of three-dimensional probability density plot, parallel intersecting a three-dimensional probability density plot to obtain a contour LXY; superimposing radioactive source reconstruction contours corresponding to N spatial layers sequentially to obtain a three-dimensional reconstruction model of a radioactive source. The method of the present invention can be applied to the nuclear electrical field, to achieve the rapid reconstruction of radioactive sources after nuclear accidents and accurate contour reconstruction after decommissioning of radioactive sources.