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• RAG4TSFMs significantly outperforms state-of-the-art baselines in prediction accuracy for PRSOV predictive maintenance.result 90%
• Existing time-series RAG approaches fail to distinguish similar regimes in scarce, transient, and covariate coupled scenarios.limitation 85%
• RAG4CTS uses a hierarchal time-series native knowledge base for lossless storage and physics-informed retrieval of raw historical regimes.method 90%
• RAG4CTS employs a two-stage bi-weighted retrieval mechanism that aligns historical trends through point-wise and multivariate similarities.method 90%
• RAG4CTS uses an agent-driven strategy to dynamically optimize context in a self-supervised manner for context augmentation.method 90%
• The PRSOV predictive maintenance scenario is characterized by data scarcity, short transient sequences, and covariate coupled dynamics.technical 90%
• The proposed system has been successfully deployed in Apache IoTDB within China Southern Airlines.market 95%
• Since deployment, the method has successfully identified one PRSOV fault in two months with zero false alarm.result 95%

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