Predictive Maintenance for Industrial Machinery Using LLM

Abstract

In industrial environments, unexpected machinery failures can result in expensive downtime along with production delays and increased maintenance expenses. Maintenance strategies, such as reactive and preventive maintenance, which are the traditional methods, often fail to provide timely interventions, leading to inefficiencies in industrial operations. This paper presents a predictive maintenance system that integrates Internet of Things (IoT) technology and Large Language Models (LLMs) to monitor and analyze real-time machine health data. The proposed system utilizes Raspberry Pi Pico W, interfaced with temperature, vibration, and current sensors to collect real time operational data. This data is transmitted to Firebase Realtime Database, where it is processed using Lang Chain powered LLMs. The system employs FAISS-based similarity search to retrieve past sensor patterns and generate predictive insights on potential failures. A Flask-based web interface enables real-time monitoring, while an automated alert system via Brevo notifies users of high-risk anomalies, allowing for proactive maintenance decisions. The results demonstrate that integrating IoT-driven real-time monitoring with AI-powered predictive analytics enhances the accuracy of failure detection, reduces unplanned downtime, and improves machine reliability. This work highlights a scalable and cost-effective approach to intelligent predictive maintenance, paving the way for more efficient industrial operations. Future enhancements include the integration of advanced deep learning models and additional sensor modalities to further refine predictive accuracy.