Aviation Fuel Pump Fault Diagnosis Based on Conditional Variational Self-Encoder Adaptive Synthetic Less Data Enhancement

Tiejun Liu, Yaoping Zhang, Xiaojing Yin () and Weidong He
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Tiejun Liu: Faculty of Aviation Foundations, Aviation University Air Force, Changchun 130012, China
Yaoping Zhang: Faculty of Aviation Foundations, Aviation University Air Force, Changchun 130012, China
Xiaojing Yin: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
Weidong He: The School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China

Mathematics, 2025, vol. 13, issue 14, 1-18

Abstract: The aircraft fuel pump is a critical component of the aviation fuel supply system, and its fault diagnosis is essential in ensuring flight safety. However, in practical operating conditions, fault samples are scarce and data distributions are highly imbalanced, which severely limits the ability of traditional models to identify minority-class faults. To address this challenge, this paper proposes a fault diagnosis method for aircraft fuel pumps based on adaptive synthetic data augmentation using a Conditional Variational Autoencoder (CVAE). The CVAE generates semantically consistent and feature-diverse minority-class samples under class-conditional constraints, thereby enhancing the overall representational capacity of the dataset. Simultaneously, the Adaptive Synthetic (ADASYN) approach adaptively augments hard-to-classify samples near decision boundaries, enabling fine-grained control over sample distribution. The integration of these two techniques establishes a “broad coverage + focused refinement” augmentation strategy, effectively mitigating the class imbalance problem. Experimental results demonstrate that the proposed method significantly improves the recognition performance of minority-class faults on real-world aircraft fuel pump fault datasets.

Keywords: conditional variational autoencoder adaptive synthetic; fault diagnosis; aviation fuel pumps; data imbalance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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