Few-Shot Hybrid Time Series and Large Language Model Framework for Air Leak Fault Diagnosis in Pressurized Pneumatic Systems

This study presents a novel few-shot multimodal framework for air-leak fault diagnosis in pressurized pneumatic systems, integrating learned multivariate time-series representations with large language model (LLM) based reasoning. The key contribution is the coupling of a convolutional autoencoder Prototypical Network with instruction-tuned LLMs through fixed and confidence weighted probability fusion, enabling complementary temporal and statistical information to be exploited under limited labeled data. The framework was validated on real-world MetroPT-3 compressor telemetry and achieved 99.0% accuracy, 99.1% precision, 99.0% F1-score, and 1.000 AUC with the Phi-3.5-mini hybrid. Moreover, confidence weighted fusion improved probability estimation, demonstrating that the proposed integration can enhance both fault discrimination and prediction reliability in few-shot industrial diagnosis.