Аbstract. Ensuring flight safety and improving the economic efficiency of aircraft maintenance require intelligent analysis of unstructured textual reports. Traditional topic modelling methods are limited by the loss of semantic context in short messages and the high labour intensity of manual text preprocessing. The subject of this study is methods for the automated extraction of knowledge from technical reports. To achieve this objective, the tasks of designing an optimal computational pipeline, implementing it in software, and conducting experimental validation using real-world data were addressed. The aim of the study is to develop, theoretically substantiate, and experimentally verify a comprehensive method for the automatic identification and detailed interpretation of latent fault subgroups. The proposed method is based on the integration of a transformer model (paraphrase-multilingual-MiniLM-L12-v2) for generating contextual embeddings, the UMAP nonlinear dimensionality reduction algorithm, and HDBSCAN hierarchical density-based clustering. Topic interpretation and the automatic generation of human-readable topic labels are implemented using the c-TF-IDF algorithm, KeyBERTInspired semantic selection, and a locally deployed large language model, Qwen3.5-4B-Instruct. Experimental validation of the method was performed on a dataset comprising 1,971 textual records related to passenger equipment collected over a seven-year operational period from nine aircraft. The proposed method successfully identified seven stable and meaningful defect categories while isolating 11.7% of atypical records into a noise cluster. Comparison with a random baseline confirmed the high statistical significance of the results. The topic diversity score reached 0.8286, which falls within the recommended range. The proposed method outperforms the classical LDA model in terms of the component-wise purity of the extracted topics and eliminates the need for complex manual data preprocessing. The solution is recommended for scaling to other sections of the ATA 100 standard and for integration into intelligent decision-support systems of aviation enterprises to optimise maintenance planning and component inventory management.

Keywords: maintenance, air transport, text mining, contextual embeddings, density-based clustering, topic modeling, large language models.