Avionics fault classification based on improved Word2Vec word embedding

He Li; Xiangning Li; Peiliang Yang; Zhenwei Zhou; Liye Cheng; Danni Hong

doi:10.1117/12.3035071

11 July 2024 Avionics fault classification based on improved Word2Vec word embedding

He Li, Xiangning Li, Peiliang Yang, Zhenwei Zhou, Liye Cheng, Danni Hong

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321036 (2024) https://doi.org/10.1117/12.3035071
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

In this paper, we present a fault classification approach that utilizes aeronautical text data by using improved Word2Vec and Convolutional Neural Network (CNN) models for fault diagnosis. The Word2Vec model is used for vectorization, and the fusion of word vector, character vector and N-gram method is used for text representation. In addition, the vector constructed based on the method are used as inputs to the CNN model, which is capable of extracting fault feature from the text data. The experimental results demonstrate that in comparison with other approaches, the method presented in this article has a better classification effect, the model has a certain degree of improvement in various indexes. In practice, it effectively decreases the manual reliance on fault text classification, and achieves the optimal performance indexes in accuracy, recall and f1-score, indicating the effectiveness of the method proposed in this paper.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

He Li, Xiangning Li, Peiliang Yang, Zhenwei Zhou, Liye Cheng, and Danni Hong "Avionics fault classification based on improved Word2Vec word embedding", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321036 (11 July 2024); https://doi.org/10.1117/12.3035071

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KEYWORDS

Data modeling

Feature extraction

Semantics

Education and training

Performance modeling

Windows

Convolution

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