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

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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.

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Authors and Affiliations

  1. Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, 710038, China

    De-long Feng, Ming-qing Xiao, Hai-fang Song, Zhao Yang & Ze-wen Hu

  2. Air Force Xi’an Flight Academy, Xi’an, 710306, China

    Ying-xi Liu

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  1. De-long Feng
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  2. Ming-qing Xiao
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  4. Hai-fang Song
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  5. Zhao Yang
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  6. Ze-wen Hu
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Correspondence to De-long Feng.

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ORCID: De-long FENG, http://orcid.org/0000-0002-6274-0720

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Feng, Dl., Xiao, Mq., Liu, Yx. et al. Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks. Frontiers Inf Technol Electronic Eng 17, 1287–1304 (2016). https://doi.org/10.1631/FITEE.1601365

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  • DOI: https://doi.org/10.1631/FITEE.1601365

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