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