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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Vydáno v:Chinese Control Conference s. 4835 - 4840
Hlavní autoři: He, ShiJie, Wang, Zhi-Ming, Liu, Kun-Zhi, Sun, Xi-Ming
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
Témata:
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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Shrnutí: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.
ISSN:1934-1768
DOI:10.23919/CCC63176.2024.10662274