As a critical component of the engine, the failure of aviation fuel pumps can lead to serious safety accidents, necessitating the development of effective maintenance programs. Fault Tree Analysis (FTA) has a clear structure and strong interpretability in maintenance decision making. However, it heavily relies on expert knowledge, which is subject to uncertainty and incoherence. Therefore, this paper proposes the NOA (Nutcracker Optimization Algorithm)–GNN (Graph Neural Network) model to enhance the accuracy and robustness of FTA by mitigating the uncertainty and inconsistency in expert knowledge. The NOA algorithm efficiently searches the solution space to identify globally optimal solutions. An FTA-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) maintenance decision-making framework has also been developed. By integrating FTA with TOPSIS, the proposed method provides a comprehensive and systematic approach that combines qualitative and quantitative analyses, thereby improving the effectiveness and reliability of maintenance decision making.

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.