Large Language Model Accelerated Maintenance Insights
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Abstract
Maintenance operations play a crucial role in the efficient functioning of manufacturing plants across various industries. Legacy systems that record maintenance operation data have long been utilized to facilitate day-to-day activities, conduct investigations, and maintain records. Previous studies have leveraged this data to explore insights and patterns related to interruption causes. However, the application and scalability of these studies have been impeded by issues such as data quality, text inaccuracies, and a lack of common natural language processing tools compatible with legacy systems. The rapid advancement of generative artificial intelligence, particularly large language models (LLMs), presents opportunities to address these challenges and enable quicker insights. This paper proposes a technical architecture for expediting insights into maintenance operation data through LLM-enabled data augmentation, summarization, and extraction, as well as embedding-based feature extraction and downstream clustering. A use case is presented based on maintenance data from an aerospace manufacturing plant, with user interviews conducted to evaluate the generated insights and system feedback. This work pioneers the adoption of LLMs in accelerating insights from under-utilized maintenance operation data, paving the way for an LLM-powered maintenance co-pilot.
How to Cite
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Large Language Model, maintenance insight, plant maintenance, maintenance co-pilot
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