用卡尔曼滤波优化原型表示，实现少样本故障检测。
Kalman Prototypical Networks for Few-shot Fault Detection in Combined Cycle Gas Turbines
- 将类别原型建模为动态系统中的随机状态，降低采样方差。
- 在仿真数据上，准确率显著优于匹配网络等主流方法。
- 适合标注数据稀缺的工业设备故障诊断场景。
联合循环燃气轮机（CCGT）在现代电力生产中具有关键作用，兼具高效率与低环境影响。然而其复杂的热流体与机械耦合关系使故障检测困难，尤其在标签故障数据稀缺时。本文提出卡尔曼原型网络（KPN），一种面向CCGT故障诊断的度量学习少样本框架。通过将类别原型建模为动态系统中的潜在随机状态，减少任务间的方差，增强嵌入表示的鲁棒性。基于高保真Modelica动态仿真构建的海上CCGT系统生成了包含正常运行及渐进泄漏故障的仿真数据集，涵盖瞬态工况。在模拟泄漏故障检测任务中，KPN在不同支持集与查询配置下均优于匹配网络、关系网络和MAML等传统少样本方法，在准确率与稳定性上表现更优。该框架通过稳定类别表示，显著提升训练收敛速度与泛化能力，适用于真实世界中标签数据有限的CCGT故障检测。
原文摘要 · Abstract (English)
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.
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