# Kalman Prototypical Networks for Few-shot Fault Detection in Combined Cycle Gas Turbines

arXiv: https://arxiv.org/abs/2606.26710 · HTML: https://mlsanity.com/paper/2606.26710/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models](https://mlsanity.com/paper/1908.01529/) (2019)
- [Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2007.12851/) (2020)
- [Sensor Fault Detection, Isolation and Identification Using Multiple Model-based Hybrid Kalman Filter for Gas Turbine Engines](https://mlsanity.com/paper/1505.02063/) (2015)
- [Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT](https://mlsanity.com/paper/2601.01701/) (2026)

## Built on by (most cited first, 1 of 1)

- [Robust Prototypical Networks for Few-Shot Sensor Fault Diagnosis](https://mlsanity.com/paper/2609.12287/) (2026)

## Abstract (arXiv)

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.