Large Language Models for Predictive Maintenance in the Leather Tanning Industry: Multimodal Anomaly Detection in Compressors

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.