AI Agents Clean Maintenance Logs for Smarter Predictive Maintenance

Kari Jaaskelainen

10 Nov 2025 — 1 min read

Predictive maintenance means fixing equipment before it fails. But in the auto industry, messy maintenance logs—full of typos, missing fields, near-duplicates, and wrong dates—can derail the machine-learning models that power those predictions.

This study tests large language model (LLM) agents as smart cleaners for those logs. The team evaluates them across six types of data noise and finds:

Strong performance on generic cleanup tasks
Room for improvement on domain-specific errors
A practical path toward faster, more reproducible PdM pipelines

Why it matters: Cleaner data means more reliable predictions, less downtime, and lower costs—especially where budgets, public datasets, and specialized expertise are scarce.

What’s next: domain-tuned training and richer agent workflows could unlock even better results.

Read the paper: http://arxiv.org/abs/2511.05311v1

Paper: http://arxiv.org/abs/2511.05311v1

Register: https://www.AiFeta.com

AI LLM PredictiveMaintenance Automotive DataCleaning MaintenanceLogs MachineLearning MLOps Industry40 Reliability

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