Kalman Prototypical Networks for Few-shot Fault Detection in Combined Cycle Gas Turbines
For industrial fault diagnosis where labeled data is scarce, this work provides a more robust and accurate few-shot learning method tailored to CCGT systems.
The paper introduces Kalman Prototypical Networks (KPN) for few-shot fault detection in combined-cycle gas turbines, outperforming conventional few-shot learning methods like Matching Networks, Relation Networks, and MAML in accuracy and stability on simulated leak fault detection tasks.
Combined-cycle gas turbines (CCGTs) play a key role in modern power generation, offering both high efficiency and reduced environmental impact. However, their complex thermo-fluid and mechanical interactions complicate fault detection, particularly when labeled fault data are scarce. In this paper, we introduce the Kalman Prototypical Network (KPN), a metric-based few-shot learning (FSL) framework specifically tailored for CCGT fault diagnosis. We model the evolution of class prototypes as latent stochastic states in a dynamic system to reduce episodic variance and improve robustness in embedding representation. Synthetic data sets generated with a high-fidelity Modelica-based dynamic simulation of an offshore CCGT system were used, simulating both normal operation and progressive leak faults under transient conditions. Application of the proposed framework on simulated leak fault detection tasks demonstrate that KPN outperforms conventional FSL methods such as Matching Networks, Relation Networks, and MAML in both accuracy and stability under varying support and query configurations. The proposed framework significantly improves training convergence and generalization by stabilizing class representations, making it well-suited for real-world CCGT fault detection where labeled data is limited.