🤖 AI Summary
Conventional quantitative reliability analysis struggles to extract deep semantic information from unstructured maintenance logs of wind turbines, while existing machine learning methods are limited to shallow classification tasks.
Method: This paper proposes a reproducible large language model (LLM)-driven framework that employs an LLM as a “collaborative pilot” to perform four types of deep semantic analysis: failure mode identification, causal chain inference, site-to-site comparative analysis, and data quality auditing. The framework integrates industrial-scale operational logs with the LLM’s natural language understanding and reasoning capabilities to construct a semantic reliability–oriented analytical pipeline.
Contribution/Results: Evaluated on real-world wind power datasets, the framework generates actionable, expert-level diagnostic hypotheses—systematically unlocking implicit knowledge embedded in unstructured logs for the first time—and significantly advances the intelligence level of wind farm operations.
📝 Abstract
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.