Leveraging Large Language Models for Predictive Maintenance of In-House Critical Equipment

SUMMARY & CONCLUSIONSThis paper presents a comprehensive technical framework for the development and deployment of an advanced predictive maintenance capability that leverages large language models (LLMs) to transform traditional industrial asset management practices. The proliferation of sensor technologies and digital maintenance records has created vast repositories of both structured and unstructured data that remain largely underutilized by conventional predictive maintenance systems. Our approach addresses this challenge by extracting actionable insights from heterogeneous historical datasets—including structured sensor outputs, unstructured maintenance logs, technician reports, and operational documentation—to enable timely, context-aware predictions for maintenance interventions.We outline a novel system architecture that seamlessly integrates data engineering pipelines, domain-specific LLM fine-tuning methodologies, probabilistic inference engines, and human-in-the-loop feedback mechanisms. The proposed framework employs a GPT-style decoder-based architecture with 6.7 billion parameters, fine-tuned using a hybrid approach combining supervised learning with contrastive learning objectives to optimize performance on maintenance-specific tasks. The system processes multimodal inputs through sophisticated data fusion techniques that temporally align textual observations with corresponding sensor measurements, creating comprehensive equipment health assessments that incorporate both quantitative metrics and qualitative expert knowledge.Our methodology addresses critical limitations of existing predictive maintenance approaches, particularly their inability to effectively leverage the wealth of institutional knowledge captured in maintenance narratives and their reliance on handcrafted features that may not generalize across different equipment types or operational contexts. The LLM-based system demonstrates superior pattern recognition capabilities, identifying subtle semantic indicators in maintenance logs that correlate with specific failure modes while maintaining interpretability through attention mechanisms and confidence scoring.Empirical evaluations conducted on a comprehensive real-world industrial dataset comprising over 10,000 maintenance events from critical rotating and thermal equipment demonstrate substantial improvements in maintenance precision and system uptime. Compared to baseline approaches using GRU-based sequence models and random forest classifiers, our LLM-integrated system achieves a 23% increase in Mean Time Between Failures (MTBF), 15% reduction in unplanned downtime, and significant improvements in prediction precision (Precision@5 increasing from 0.41 to 0.76). Additionally, technician satisfaction scores improved from 3.2 to 4.4 on a 5-point Likert scale, indicating enhanced usability and trust in AI-assisted maintenance decision-making. The system's ability to process multi-modal inputs and generate interpretable outputs with associated confidence scores makes it particularly well-suited for deployment in safety-critical industrial environments where human oversight remains essential.