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

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 t...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 13; no. 14; p. 2218
Main Authors: Liu, Tiejun, Zhang, Yaoping, Yin, Xiaojing, He, Weidong
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2025
Subjects:
Aeronautics
Aircraft accidents & safety
Aviation fuel
aviation fuel pumps
conditional variational autoencoder adaptive synthetic
Controllers
Cooling
Critical components
Data augmentation
data imbalance
Datasets
Failure analysis
Fault detection
Fault diagnosis
Fixed base operators industry
Flight safety
Fuel pumps
Neural networks
Synthetic data
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary: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.
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content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math13142218