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公开(公告)号：US20230076346A1
公开(公告)日：2023-03-09
申请号：US17887027
申请日：2022-08-12
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Tianju SUI , Qingfeng LIU , Ximing SUN
IPC: G06F21/55
Abstract: A two-dimensionality detection method for industrial control system attacks: collecting data; transmitting the data to a PLC and an embedded attack detection system; uploading, by the PLC, received data to an SCADA system; transmitting, by the SCADA system, the data to the embedded attack detection system after classifying and counting the data; before starting detection, directly reading, by the embedded attack detection system, the data measured by sensors; refining data association relationships and probability distribution characteristics of the sensors of normal operation to complete storage of health data model; after starting detection, in first dimensionality, comparing the data collected directly by the sensors with statistical data of the SCADA system to judge the attacked condition of the SCADA system, and in second dimensionality, comparing the characteristics of the data collected directly by the sensors and counted online with the health data model to judge the attacked condition of the sensors.
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2.发明申请公开(公告)号：US20220341996A1
公开(公告)日：2022-10-27
申请号：US17311931
申请日：2021-01-20
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Ximing SUN , Aina WANG , Yingshun LI , Chongquan ZHONG
IPC: G01R31/367 , G06N3/08
Abstract: A method for predicting faults in power pack of complex equipment based on a hybrid prediction model is provided. The method includes steps of analyzing the typical faults of the power pack of complex equipment, extracting the core set of attributes therein, decomposing the time series of the power pack into a linear part and a non-linear part, using an Autoregressive Integrated Moving Average model to forecast the linear part, using an Artificial Neural Network model to forecast the residual obtained, and the predictions of the power pack are obtained by summing the predictions of the non-linear component with the linear component. The method further includes using the hybrid prediction model and the parallel parameters of the core attributes in combination with the upper and lower limits to obtain information on the operation status of the power pack.
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3.发明申请公开(公告)号：US20220121797A1
公开(公告)日：2022-04-21
申请号：US17291819
申请日：2020-07-13
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Ximing SUN , Nan DUAN , Yuhu WU , Chongquan ZHONG
Abstract: The present invention provides a method for analyzing global stability of a conveying fluid pipe-nonlinear energy sink system, and belongs to the technical field of system stability proof and analysis of a control system. The method comprises: establishing a high-order partial differential model of a conveying fluid pipe-nonlinear energy sink system based on a target energy transfer theory, discretizing the model into a second-order nonlinear ordinary differential form by means of the Galerkin approximation method, and further transforming the model into a quadratic model containing gradient information first; then obtaining a global stability judgment condition of the system by means of the energy disturbance technology, and verifying the theoretical results by means of the numerical method finally.
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公开(公告)号：US20220092428A1
公开(公告)日：2022-03-24
申请号：US17312278
申请日：2020-09-28
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Ximing SUN , Fuxiang QUAN , Hongyang ZHAO , Yanhua MA , Pan QIN
Abstract: The present invention relates to a prediction method for stall and surge of an axial compressor based on deep learning. The method comprises the following steps: firstly, preprocessing data with stall and surge of an aeroengine, and partitioning a test data set and a training data set from experimental data. Secondly, constructing an LR branch network module, a WaveNet branch network module and a LR-WaveNet prediction model in sequence. Finally, conducting real-time prediction on the test data: preprocessing test set data in the same manner, and adjusting data dimension according to input requirements of the LR-WaveNet prediction model; giving surge prediction probabilities of all samples by means of the LR-WaveNet prediction model according to time sequence; and giving the probability of surge that data with noise points changes over time by means of the LR-WaveNet prediction model, to test the anti-interference performance of the model.
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公开(公告)号：US20210201155A1
公开(公告)日：2021-07-01
申请号：US16981682
申请日：2020-02-28
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Yanhua MA , Xian DU , Ximing SUN , Weiguo XIA
Abstract: An intelligent control method for a dynamic neural network-based variable cycle engine is provided. By adding a grey relation analysis method-based structure adjustment algorithm to the neural network training algorithm, the neural network structure is adjusted, a dynamic neural network controller is constructed, and thus the intelligent control of the variable cycle engine is realized. A dynamic neural network is trained through the grey relation analysis method-based network structure adjustment algorithm designed by the present invention, and an intelligent controller of the dynamic neural network-based variable cycle engine is constructed. Thus, the problem of coupling between nonlinear multiple variables caused by the increase of control variables of the variable cycle engine and the problem that the traditional control method relies too much on model accuracy are effectively solved.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20200063665A1
公开(公告)日：2020-02-27
申请号：US16462504
申请日：2018-01-25
Applicant: Dalian University of Technology
Inventor： Yanhua MA , Xian DU , Ximing SUN
Abstract: An aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm. A dynamic parallel compensator based on a recursive neural network is adopted to compensate the error of the original nonlinear model within the full flight envelope under the condition without aero-engine performance deterioration. A modifier based on a genetic algorithm is also adopted to conduct adaptive adjustment on correction coefficients of health parameters to be modified in the original nonlinear component-level model. The health parameters to be modified are determined by a multi-attribute decision algorithm based on integrated evaluation. The sum of the modified nonlinear component-level model output and the compensator output is consistent with the aero-engine operation test output data. This provides powerful support for the design of aero-engine control systems and fault diagnosis systems.
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公开(公告)号：US20250036935A1
公开(公告)日：2025-01-30
申请号：US18280581
申请日：2023-05-17
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Ximing SUN , Aina WANG , Pan QIN
Abstract: A coupled physics-informed neural network for solving displacement distribution of a bounded vibration string under an unknown external driving force is provided. A novel PINN is proposed, called C-PINN, used for solving the displacement distribution of the bounded vibration string under an external driving force with little or even no priori information. It comprises two neural networks: NetU and NetG. NetU is used for approximating satisfying the displacement distribution of the bounded vibration string under study. NetG is used for regularizing u in the NetU to satisfy the displacement distribution of the approximation of NetU. The two networks are integrated into a data-physics-hybrid loss function. In addition, a proposed hierarchical training strategy is used for optimizing the loss function and realizing the coupling of the two networks. Finally, the performance of the C-PINN in solving the displacement distribution of the bounded vibration string under the external driving force is verified.
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公开(公告)号：US20240068907A1
公开(公告)日：2024-02-29
申请号：US18021493
申请日：2022-05-11
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Ximing SUN , Aina WANG , Yingshun LI , Pan QIN , Chongquan ZHONG
IPC: G01M13/045 , G06N3/126
CPC classification number: G01M13/045 , G06N3/126
Abstract: The present invention provides an optimization algorithm for automatically determining variational mode decomposition parameters based on bearing vibration signals. First, mode energy is used to reflect bandwidth, a bandwidth optimization sub-model is established to automatically obtain optimal bandwidth parameter αopt. Secondly, energy loss optimization sub-model is established to avoid under-decomposition. Thirdly, a mode mean position distance optimization sub-model is established to prevent the generation of too much K and avoid the phenomenon of over-decomposition. Finally, considering the interaction between the bandwidth parameter α and the total number of modes K, the interaction between mode components and the integrity of reconstruction information, nonlinear transformation is performed by a logarithmic function, so as to make the values of three optimization sub-models form similar scales, obtain an optimization model that can automatically determine optimal VMD parameters αopt and Kopt, and establish a quantitative evaluation index for the decomposition performance of a VMD algorithm.
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公开(公告)号：US20230003778A1
公开(公告)日：2023-01-05
申请号：US17625307
申请日：2021-02-22
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Ximing SUN , Ping LIN
IPC: G01R25/00
Abstract: The present invention belongs to the technical field of aviation electrics and electric power, and provides an aircraft grid phase angle tracker based on nonlinear active disturbance rejection, which is used to estimate the grid phase angle on AC side of an aircraft grid. A embedded generator in the aircraft grid is arranged inside a compressor of an aviation gas turbine engine, and the embedded generator is directly coupled with the aviation gas turbine engine so that the AC frequency of the embedded generator varies with the speed of the aviation gas turbine engine. The present invention applies the nonlinear active disturbance rejection technology to the phase angle tracking of the more electric aircraft grid, is simple in operation and high in accuracy, and can realize high-accuracy tracking of the grid phase angle. The method has certain extensibility and can be extended to other fields.
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公开(公告)号：US20220311366A1
公开(公告)日：2022-09-29
申请号：US17431326
申请日：2020-10-27
Applicant: DALIAN UNIVERSITY OF TECHNOLOGY
Inventor： Ximing SUN , Jianyi ZHANG
Abstract: The present invention belongs to the technical field of permanent magnet synchronous motor control, and provides a speed control method for a permanent magnet synchronous motor considering current saturation and disturbance suppression, which aims to effectively ensure that a current of the motor is always within a given range to avoid the problem of control performance reduction caused by the fact that the current gets into a saturation state, ensure the safety of a system, do not need to use unavailable state variables such as motor acceleration and the like, effectively estimate and compensate disturbances including parameters uncertainty and unknown load torque disturbance existing in a permanent magnet synchronous motor system, and rapidly and accurately control a speed of the motor finally. There is no need to configure a plurality of sensors in practical industrial application, so system building costs can be reduced on the one hand, and the stability of the system can be improved on the other hand. In conclusion, the technical solution proposed by the present invention has important practical application significance.
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