Fault diagnosis of aircraft engines based on multi-sensor fusion sparse denoising autoencoder

As the heart of an aircraft, it is crucial to accurately grasp the operational status of aeroengines. However, it is difficult to fully reflect the accurate fault status through a single sensor signal, and the diagnostic effect of using input signals in complex environments is not satisfactory. Ther...

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Vydané v:Chinese Control Conference s. 4835 - 4840
Hlavní autori: He, ShiJie, Wang, Zhi-Ming, Liu, Kun-Zhi, Sun, Xi-Ming
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
Predmet:
Accuracy
aeroengine
Atmospheric modeling
Fault diagnosis
Feature extraction
multi-sensor information fusion
Noise
Noise reduction
sparse denoising autoencoder
Training
ISSN:1934-1768
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Abstract As the heart of an aircraft, it is crucial to accurately grasp the operational status of aeroengines. However, it is difficult to fully reflect the accurate fault status through a single sensor signal, and the diagnostic effect of using input signals in complex environments is not satisfactory. Therefore, this article proposes an aeroengine fault diagnosis method based on multisensor fusion and sparse denoising autoencoder, which comprehensively considers multi-sensor decision-making and reduces the difficulty of extracting high-dimensional data features, reducing the interference caused by noise. Experiments have shown that this method can effectively identify the fault status of aircraft engines, and compared to existing methods, this method can provide more accurate diagnostic results.
AbstractList As the heart of an aircraft, it is crucial to accurately grasp the operational status of aeroengines. However, it is difficult to fully reflect the accurate fault status through a single sensor signal, and the diagnostic effect of using input signals in complex environments is not satisfactory. Therefore, this article proposes an aeroengine fault diagnosis method based on multisensor fusion and sparse denoising autoencoder, which comprehensively considers multi-sensor decision-making and reduces the difficulty of extracting high-dimensional data features, reducing the interference caused by noise. Experiments have shown that this method can effectively identify the fault status of aircraft engines, and compared to existing methods, this method can provide more accurate diagnostic results.
Author Liu, Kun-Zhi
Sun, Xi-Ming
He, ShiJie
Wang, Zhi-Ming
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  organization: Dalian University of Technology,School of Control Science and Control Engineering,Dalian,China,116024
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  givenname: Xi-Ming
  surname: Sun
  fullname: Sun, Xi-Ming
  email: sunxm@dlut.edu.cn
  organization: Dalian University of Technology,School of Control Science and Control Engineering,Dalian,China,116024
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Snippet As the heart of an aircraft, it is crucial to accurately grasp the operational status of aeroengines. However, it is difficult to fully reflect the accurate...
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SubjectTerms Accuracy
aeroengine
Atmospheric modeling
Fault diagnosis
Feature extraction
multi-sensor information fusion
Noise
Noise reduction
sparse denoising autoencoder
Training
Title Fault diagnosis of aircraft engines based on multi-sensor fusion sparse denoising autoencoder
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