Full Text:   <762>

Summary:  <231>

CLC number: TP391; V267.3

On-line Access: 2016-12-13

Received: 2016-07-05

Revision Accepted: 2016-10-09

Crosschecked: 2016-11-08

Cited: 1

Clicked: 1166

Citations:  Bibtex RefMan EndNote GB/T7714


De-long Feng


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.12 P.1287-1304


Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks

Author(s):  De-long Feng, Ming-qing Xiao, Ying-xi Liu, Hai-fang Song, Zhao Yang, Ze-wen Hu

Affiliation(s):  Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an 710038, China; more

Corresponding email(s):   fengdelong101@foxmail.com

Key Words:  Deep belief networks (DBNs), Fault diagnosis, Information entropy, Engine

De-long Feng, Ming-qing Xiao, Ying-xi Liu, Hai-fang Song, Zhao Yang, Ze-wen Hu. Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(12): 1287-1304.

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author="De-long Feng, Ming-qing Xiao, Ying-xi Liu, Hai-fang Song, Zhao Yang, Ze-wen Hu",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks
%A De-long Feng
%A Ming-qing Xiao
%A Ying-xi Liu
%A Hai-fang Song
%A Zhao Yang
%A Ze-wen Hu
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 12
%P 1287-1304
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601365

T1 - Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks
A1 - De-long Feng
A1 - Ming-qing Xiao
A1 - Ying-xi Liu
A1 - Hai-fang Song
A1 - Zhao Yang
A1 - Ze-wen Hu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 12
SP - 1287
EP - 1304
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601365

Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose potential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks (DBNs) is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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