Predictive Maintenance with Large Language Models and Transformer-based Survival Analysis

Predictive maintenance involves proactive monitoring and prediction of equipment failures to optimize maintenance schedules to minimize downtime, reduce losses, and ensure optimal equipment performance. However, traditional methods often rely on classification or regression, overlooking the temporal aspect of failure prediction. Survival analysis is focused on modeling time-to-event data, making it well-suited for predicting the time until equipment failures. The primary goal of this paper is to evaluate the feasibility and effectiveness of Transformer-based model for survival analysis in the industrial domain. Additionally, this work aims to explore the integration of Large Language Models as virtual assistants to facilitate user interaction and decision-making in the predictive maintenance process. The proposed system was tested on a public predictive maintenance problem and the Transformer-based model obtained better survival predictions than the baseline statistical and machine learning models, with improvements of up to 20%. Furthermore, the virtual assistant based on the large language model demonstrated the ability to retrieve precise information based on natural language prompts.