Cleaning Maintenance Logs with LLM Agents for Improved Predictive Maintenance

Economic constraints, limited availability of datasets for reproducibility
and shortages of specialized expertise have long
been recognized as key challenges to the adoption and advancement
of predictive maintenance (PdM) in the automotive
sector. Recent progress in large language models (LLMs)
presents an opportunity to overcome these barriers and speed
up the transition of PdM from research to industrial practice.
Under these conditions, we explore the potential of LLMbased
agents to support PdM cleaning pipelines. Specifically,
we focus on maintenance logs, a critical data source
for training well-performing machine learning (ML) models,
but one often affected by errors such as typos, missing
fields, near-duplicate entries, and incorrect dates. We evaluate
LLM agents on cleaning tasks involving six distinct types
of noise. Our findings show that LLMs are effective at handling
generic cleaning tasks and offer a promising foundation
for future industrial applications. While domain-specific errors
remain challenging, these results highlight the potential
for further improvements through specialized training and enhanced
agentic capabilities.