Cleaning Maintenance Logs with LLM Agents for Improved Predictive Maintenance

Abstract

Maintenance logs serve as the backbone of data-driven Predictive Maintenance (PdM) systems by providing information that can be used to create and label datasets for training survival analysis and machine learning (ML) models. However, due to personnel manually entering information into maintenance logs and the various levels of flexibility that maintenance tracking systems allow, service records often contain errors. Currently, the cleaning of equipment maintenance records is performed manually by experts such as data scientists or reliability engineers. Nevertheless, this task is time-consuming and often does not entirely eliminate noise from the data. In this paper, we propose using large language model (LLM)-based agents to automate the cleaning of maintenance logs. We provide an implementation that allows the agents to perform data cleaning as well as metrics to assess agents' performance. Finally, we compare the performance of several LLMs on this task. Our empirical results indicate that LLM-based agents are a promising solution for improving the quality of the datasets used in PdM systems and ultimately developing predictive maintenance models that are more reliable and useful.