The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 01, 2021

Filed:

Nov. 28, 2019
Applicant:

Dalian University of Technology, Liaoning, CN;

Inventors:

Yanhua Ma, Liaoning, CN;

Xian Du, Liaoning, CN;

Ximing Sun, Liaoning, CN;

Yuhu Wu, Liaoning, CN;

Yanlei Gao, Liaoning, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01M 15/05 (2006.01); G06N 3/04 (2006.01); B64F 5/60 (2017.01); G05B 23/02 (2006.01);
U.S. Cl.
CPC ...
G01M 15/05 (2013.01); B64F 5/60 (2017.01); G05B 23/0283 (2013.01); G06N 3/0472 (2013.01); F05D 2260/80 (2013.01); G05B 2219/45071 (2013.01);
Abstract

A stochastic configuration network based turbofan engine health parameter estimation method is disclosed. The stochastic configuration network based turbofan engine health parameter estimation method designed by the present invention combines the model based Kalman filter algorithm and the data-driven based stochastic configuration network, i.e. using the output of the stochastic configuration network as the compensation of the Kalman filter algorithm, so as to take into account the estimated result of the Kalman filter and the estimated result of the stochastic configuration network and improve the estimation accuracy of the original Kalman filter algorithm when the measurable parameters of the turbofan engine are less than the health parameters to be estimated. In addition, the present invention effectively reduces the accuracy loss caused by the poor structure of the neural network through the stochastic configuration network, and improves the generalization ability of the network.


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