A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Zhou, Shenghan; Wei, Chenxi; Li, Pan; Liu, Anying; Chang, Wenbing; Xiao, Yiyong

doi:10.3390/aerospace8120357

Cited by 8 publications

(2 citation statements)

References 34 publications

(34 reference statements)

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“…Throughout the life cycle of aircraft, a significant number of documents related to failures are generated [4], including FMECA (Failure Mode, Effect, and Criticality Analysis) documents, aircraft maintenance manuals, diagnostic records, and fault analysis reports, among others. These resources provide valuable knowledge for the diagnosis of aircraft faults [5,6]. However, due to the heterogeneity of the data, these resources exist as fragmented "information islands" [7], hindering effective knowledge sharing and leading to inefficient resource utilization.…”

Section: Introductionmentioning

confidence: 99%

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

Tang

Cui

et al. 2023

Sensors

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Fault diagnosis is crucial for repairing aircraft and ensuring their proper functioning. However, with the higher complexity of aircraft, some traditional diagnosis methods that rely on experience are becoming less effective. Therefore, this paper explores the construction and application of an aircraft fault knowledge graph to improve the efficiency of fault diagnosis for maintenance engineers. Firstly, this paper analyzes the knowledge elements required for aircraft fault diagnosis, and defines a schema layer of a fault knowledge graph. Secondly, with deep learning as the main method and heuristic rules as the auxiliary method, fault knowledge is extracted from structured and unstructured fault data, and a fault knowledge graph for a certain type of craft is constructed. Finally, a fault question-answering system based on a fault knowledge graph was developed, which can accurately answer questions from maintenance engineers. The practical implementation of our proposed methodology highlights how knowledge graphs provide an effective means of managing aircraft fault knowledge, ultimately assisting engineers in identifying fault roots accurately and quickly.

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Section: Introductionmentioning

confidence: 99%

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

Tang

Cui

et al. 2023

Sensors

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“…Unlike the integration methods of bagging and boosting, Stacking uses a meta-learner to combine the output features of the base learners to increase the generalization ability, thus improving the model's prediction accuracy and reducing the model's risk of overfitting. Stacking ensemble learning has made great strides in medical and mechanical fault diagnosis in recent years [21,22].…”

Section: Introductionmentioning

confidence: 99%

APVC2021-A Gas Leakage Detection Method with Hybrid Acoustic Feature Selection and Stacking Ensemble Learning

et al. 2023

Preprint

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Model’s generalization and feature selection are always two challenging problems for gas leakage detection. This paper presents a method for gas leakage detection based on Hybrid-Feature-Selection-Stacking ensemble learning (HFS-Stacking), which fuses the Hybrid Feature Selection and Stacking ensemble learning. Firstly, a hybrid feature selection algorithm (HFS), which combines SFS-SVM, SFS-KNN, RFE-RF, RFE-XGB, and MIC algorithms, is proposed to select the optimal feature subset from multiple commonly used acoustic signal features; Then, SVM, KNN, random forest and XGBoost are designed as base learners in the stacking integration framework, which increase the generalization ability of the model. XGBoost is used as the meta-learner to output the classification results. The proposed gas leakage detection method gets the optimal subset of features and speeds of the subsequent model. Meanwhile, the constructed model can effectively improve the indicator of ,, and, and has a good generalization ability. The experimental results show that the HFS-Stacking algorithm using fewer features can effectively improve the training speed, accuracy, F1-score, AUC value and recall rate, and also has better robustness.

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A literature review of fault diagnosis based on ensemble learning

Mian,

Deng,

Dong

et al. 2024

Engineering Applications of Artificial Intelligence

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A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Cited by 8 publications

References 34 publications

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

APVC2021-A Gas Leakage Detection Method with Hybrid Acoustic Feature Selection and Stacking Ensemble Learning

A literature review of fault diagnosis based on ensemble learning

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