A Novel Operations-Based Application of Natural Language Processing to Enhance Aircraft System Troubleshooting



Published Oct 26, 2023
Jamie Asbach Daniel Wade


Troubleshooting an aircraft system is difficult. With flights often logging hundreds, or even thousands, of codes, the task of isolating the root cause of an issue is a complex undertaking. By leveraging Natural Language Processing techniques such as Word2Vec, artificial intelligence can be used to extract patterns from the context of these faults. Treating the fault codes issued by the on-board system in an aircraft as the “words” which make up a body of text, a model can be trained to understand the patterns of this language in a similar approach to how natural language is processed by computers to discretize the order and structure of human language. By assessing the cosine similarity of vectorized fault sequences used to train the model, faults occurring in similar sequences can be extracted, resulting in improved troubleshooting. The result of this effort is a tool to aid maintainers in isolating faults by quantifying the relations between the different codes and analyzing the patterns in which they occur. The benefits of such a tool include significant reduction in time and cost in aircraft maintenance by avoiding unnecessary exploratory maintenance.

How to Cite

Asbach, J., & Wade, D. (2023). A Novel Operations-Based Application of Natural Language Processing to Enhance Aircraft System Troubleshooting. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3579
Abstract 305 | PDF Downloads 246



Large Language Model, Artificial Intelligence, aircraft, aircraft maintenance, natural language processing, aerospace, fault isolation, sympathetic faults, troubleshooting

Pypi word2vec. (2023, 5 17). Retrieved from word2vec: https://pypi.org/project/word2vec/

(2023, 05 17). Retrieved from dash_bio.Circos Examples and Reference: https://dash.plotly.com/dash-bio/circos

Ezhilarasu, C. M. (2019). The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities. Progress in Aerospace Sciences, 14.

Heisey, R. (n.d.). Maintenance Costs. Retrieved from boeing.com: https://www.boeing.com/commercial/aeromagazine/aero_19/717_story.html

Introduction to Word Embedding and Word2Vec. (2023, 05 17). Retrieved from Towards Data Science: https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa

Kala, M. (2022). Analyzing Aircraft Maintenance Findings with Natural Language. 11th International Conference on Air Transport – INAIR 2022, 8.

Krzywinski, M. (2009). Circos: an information aesthetic for comparative geonomics.

word2vec Tutorial. (2022, 12 20). Retrieved from TensorFlow: https://www.tensorflow.org/tutorials/text/word2vec
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