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.

公开(公告)号：US20250045347A1

公开(公告)日：2025-02-06

申请号：US18713854

申请日：2023-08-17

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Ximing SUN ,  Aina WANG ,  Pan QIN ,  Hongxin LI

IPC: G06F17/13

Abstract: The present invention provides a spatiotemporal dynamic system soft sensing method for automatically determining a partial differential equation (PDE) structure and belongs to the technical field of soft sensing of neural networks. Firstly, a loss function for training a coupled physics-informed neural network with a recurrent prediction mechanism is constructed to obtain a solution and a driving source which satisfy a PDE used for describing spatiotemporal industrial processes; secondly, differential operator candidates are obtained by an automatic differentiation method, and an appropriate PDE structure is selected from the differential operator candidates to accurately describe the spatiotemporal industrial processes; and finally, the soft sensing result is verified using heat diffuse phenomena and actual vibration processes. The CPINNRP-AIC is suitable for soft sensing methods of multi-class dynamic systems with spatiotemporal dependence, can achieve the effective acquisition of key variable values for high-end complex equipment such as an aero-engine in operation processes.

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

IPC: G06N3/08 ,  G01M99/00 ,  G06N3/045

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.

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.