Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models
This work addresses the challenge of analyzing unstructured maintenance data for the wind energy sector, providing a novel framework to enhance operational intelligence, though it is incremental in applying LLMs to a specific domain.
The paper tackled the problem of extracting operational intelligence from unstructured wind turbine maintenance logs by using large language models (LLMs) for deep semantic analysis, moving beyond simple classification to perform tasks like failure mode identification and causal chain inference, with results showing LLMs can generate actionable, expert-level hypotheses. This approach offers a reproducible methodology to unlock insights previously hidden in the data.
A wealth of operational intelligence is locked within the unstructured free-text of wind turbine maintenance logs, a resource largely inaccessible to traditional quantitative reliability analysis. While machine learning has been applied to this data, existing approaches typically stop at classification, categorising text into predefined labels. This paper addresses the gap in leveraging modern large language models (LLMs) for more complex reasoning tasks. We introduce an exploratory framework that uses LLMs to move beyond classification and perform deep semantic analysis. We apply this framework to a large industrial dataset to execute four analytical workflows: failure mode identification, causal chain inference, comparative site analysis, and data quality auditing. The results demonstrate that LLMs can function as powerful "reliability co-pilots," moving beyond labelling to synthesise textual information and generate actionable, expert-level hypotheses. This work contributes a novel and reproducible methodology for using LLMs as a reasoning tool, offering a new pathway to enhance operational intelligence in the wind energy sector by unlocking insights previously obscured in unstructured data.