A Semi-Supervised Siamese Network for Complex Aircraft System Fault Detection with Limited Labeled Fault Samples

Xinyun, Zhu; Sun, Jianzhong; Hu, Hanchun; Li, Chunhua

doi:10.17531/ein/174382

Artykuł - szczegóły

Tytuł artykułu

A Semi-Supervised Siamese Network for Complex Aircraft System Fault Detection with Limited Labeled Fault Samples

Autorzy

Xinyun Zhu , Sun Jianzhong , Hu Hanchun , Li Chunhua

Treść / Zawartość

Pełne teksty:

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DOI

10.17531/ein/174382

Warianty tytułu

Języki publikacji

Abstrakty

Health monitoring and fault detection of complex aircraft systems are paramount for ensuring reliable and efficient operation. The availability of monitoring data from modern aircraft onboard sensors provides a wealth of big data for developing deep learning-based fault detection methods. However, aircraft onboard systems typically have limited labeled fault samples and large amounts of unlabeled data. To better utilize the information contained in limited labeled fault samples, a deep learning-based semi-supervisedfault detection method is proposed, which leverages a small number of labeled fault samples to enhance its performance. A novel sample pairing strategy is introduced to improve algorithm performance by iteratively utilizing fault samples. A comprehensive loss function is employed to accurately reconstruct normal samples and effectively separate fault samples. The results of a case study using real data from a commercial aircraft fleet demonstrate the superiority of the proposed method over existing techniques, with improvements of approximately 16.7% in AP, 9.5% in AUC, and 19.2% in F1 score. Ablation studies confirm that performance can be further improved by incorporating additional labeled fault samples during training. Furthermore, the algorithm demonstrates good generalization ability.

Słowa kluczowe

fault detection semi-supervised aircraft system flight data time-series data

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2023

Tom

Vol. 25, no. 4

Strony

art. no. 174382

Opis fizyczny

Bibliogr. 64 poz., rys., tab., wykr.

Twórcy

autor

Xinyun Zhu

zhuxinyun@nuaa.edu.cn

Nanjing University of Aeronautics and Astronautics, China

https://orcid.org/0000-0002-1813-6710

autor

Sun Jianzhong

sunjianzhong@nuaa.edu.cn

Nanjing University of Aeronautics and Astronautics, China

https://orcid.org/0000-0003-1806-7388

autor

Hu Hanchun

acfun0218@164.com

Nanjing University of Aeronautics and Astronautics, China

autor

Li Chunhua

nuaaccalch@nuaa.edu.cn

Nanjing University of Aeronautics and Astronautics, China

Bibliografia

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