Work

Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement

Year: 2024

Type: article

Abstract: 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.

Source: Mathematics

DOI: 10.3390/math13010123

Authors: Weidong He, Xiaojing Yin, Yubo Shao, Dianxin Chen, Jianglong Mi, Yang Jiao

Institutions: Changchun University of Technology, Jilin University, PLA Air Force Aviation University

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funder: National Natural Science Foundation of China