Large Language Models for Predictive Maintenance in the Leather Tanning Industry: Multimodal Anomaly Detection in Compressors
Giulia Palma, Gaia Cecchi, Antonio Rizzo
Abstract
Predictive maintenance in industrial settings increasingly demands systems capable of integrating heterogeneous data streams while balancing computational efficiency and contextual reasoning. This paper introduces a novel framework leveraging Large Language Models (LLMs) to address these challenges in compressor monitoring, demonstrating their potential to enhance anomaly detection accuracy and operational cost-effectiveness. We evaluate Qwen 2.5-32B against traditional machine learning models (ANN, CNN, LSTM), achieving superior recall (92.3%) and AUC-ROC (0.991) through transformer-based architectures optimized for multimodal data fusion. A financial case study reveals operational cost reductions of 18% via reduced downtime and optimized maintenance schedules, while a real-time monitoring dashboard validates scalability for industrial deployment. Our findings highlight the transformative role of LLMs in bridging technical innovation with domain-specific operational constraints, offering a blueprint for predictive maintenance in niche industries.