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FITEE
Front. Inform. Technol. Electron. Eng.  2016, Vol. 17 Issue (12): 1287-1304    DOI: 10.1631/FITEE.1601365
    
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
De-long Feng, Ming-qing Xiao, Ying-xi Liu, Hai-fang Song, Zhao Yang, Ze-wen Hu
Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China; Air Force Xi'an Flight Academy, Xi'an 710306, China
Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks
De-long Feng, Ming-qing Xiao, Ying-xi Liu, Hai-fang Song, Zhao Yang, Ze-wen Hu
Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China; Air Force Xi'an Flight Academy, Xi'an 710306, China
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摘要： 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    
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.
Key words: Deep belief networks (DBNs)    Fault diagnosis    Information entropy    Engine
收稿日期: 2016-07-05 出版日期: 2016-12-13
CLC:  TP391  
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De-long Feng
Ming-qing Xiao
Ying-xi Liu
Hai-fang Song
Zhao Yang
Ze-wen Hu

引用本文:

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. Front. Inform. Technol. Electron. Eng., 2016, 17(12): 1287-1304.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/FITEE.1601365    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2016/V17/I12/1287

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