A text mining based fault diagnosis model for aircraft

Chaofan Wei; Wei Zhang; Yanyan Yin; Yu Zhang; Ting Hu; Xin Xu

doi:10.1117/12.3035084

19 July 2024 A text mining based fault diagnosis model for aircraft

Chaofan Wei, Wei Zhang, Yanyan Yin, Yu Zhang, Ting Hu, Xin Xu

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131D (2024) https://doi.org/10.1117/12.3035084
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Aiming at the phenomenon that the aircraft fault text data have not been sufficiently exploited, this paper proposed an intelligent aircraft fault diagnosis model based on natural language processing methodology. The proposed model firstly used word2vec to generate 100-dimensional word vectors for every word. Then the extracted text features were input into the LightGBM classification model for further fault diagnosis. Moreover, the Borderline SMOTE algorithm is used to make up the shortage caused by the imbalanced data. We conducted validation experiments on a real-world dataset that recorded by a long-term maintenance and support work. The experimental results indicates that the overall performance of the proposed model is better than other comparison models, which is of significant guidance and reference for the maintainers to take actions to make the aircraft return to normal.

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

Citation Download Citation

Chaofan Wei, Wei Zhang, Yanyan Yin, Yu Zhang, Ting Hu, and Xin Xu "A text mining based fault diagnosis model for aircraft", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131D (19 July 2024); https://doi.org/10.1117/12.3035084

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KEYWORDS

Data modeling

Performance modeling

Feature extraction

Mining

Histograms

Education and training

Semantics

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