Full Text:   <888>

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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: 1606

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

De-long Feng

http://orcid.org/0000-0002-6274-0720

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

http://doi.org/10.1631/FITEE.1601365


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",
volume="17",
number="12",
pages="1287-1304",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601365"
}

%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

TY - JOUR
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


Abstract: 
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.

基于信息熵和深度置信网络的涡轮发动机在有限传感器下的故障诊断仿真研究

概要:精确故障诊断是预测与健康管理的一个重要部分。它能避免事故的发生,延长设备使用寿命,还能降低设备维修保养费用。本文研究涡轮发动机的故障诊断。由于发动机工作在高温、高压、高转速的严峻环境中,不能安装过多传感器,因此我们无法获得足够多的传感器数据,以至于采用现有算法不能进行精确的潜在故障诊断。本文针对复杂环境下有限传感器数据的发动机故障诊断问题,提出了一种基于信息熵的深度置信网络方法。首先介绍了几种信息熵,并基于单信号熵提出了联合复杂信息熵。其次,分析了深度置信网络的构成,提出了基于信息熵的深度置信网络方法。验证实验表明,与现有的机器学习算法比较,该方法的诊断精度大大提高。

关键词:深度置信网络;信息熵;故障诊断;发动机

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

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