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：US20240133391A1

公开(公告)日：2024-04-25

申请号：US18014573

申请日：2022-02-22

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Yingshun LI ,  Ziqin GONG ,  Ximing SUN ,  Fuyang QUAN

IPC: F04D27/00 ,  G06N3/047

CPC classification number: F04D27/001 ,  G06N3/047

Abstract: The present invention provides a prediction method for stall and surging of an axial-flow compressor based on a deep autoregressive network. Firstly, selecting and preprocessing surging experimental data of a certain type of aero-engine, and dividing the data into a training set and a test set. Secondly, building and training a deep autoregressive network model based on an attention mechanism, using the finally trained model to conduct real-time prediction on the test set, and giving a model loss and an evaluation index. Finally, using a prediction model to conduct real-time prediction on the test data, and giving a trend of surging probability varying with time in chronological order. The present invention uses the attention mechanism to effectively capture the features of the experimental data and accurately predict the surging probability, which improve the stability and accuracy of prediction, is beneficial to improving the performance of active control of the engine.

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.