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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| Published in: | Chinese Control Conference pp. 4835 - 4840 |
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| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
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Technical Committee on Control Theory, Chinese Association of Automation
28.07.2024
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| 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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: ShiJie surname: He fullname: He, ShiJie email: hesj@mail.dlut.edu.cn organization: Dalian University of Technology,School of Control Science and Control Engineering,Dalian,China,116024 – sequence: 2 givenname: Zhi-Ming surname: Wang fullname: Wang, Zhi-Ming email: wangzhimin_1994@mail.dlut.edu.cn organization: Dalian University of Technology,School of Control Science and Control Engineering,Dalian,China,116024 – sequence: 3 givenname: Kun-Zhi surname: Liu fullname: Liu, Kun-Zhi email: kzliu1989@dlut.edu.cn organization: Dalian University of Technology,School of Control Science and Control Engineering,Dalian,China,116024 – sequence: 4 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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