Physics-Informed Real NVP for Satellite Power System Fault Detection

TL;DR

This paper introduces a physics-informed Real NVP model for satellite fault detection, demonstrating superior performance over traditional AI methods in space system diagnostics, emphasizing robustness and reliability.

Contribution

The study presents a novel physics-informed Real NVP approach tailored for satellite fault detection, outperforming existing AI techniques and highlighting the benefits of physics-informed loss functions.

Findings

Outperforms Gated Recurrent Unit and Autoencoder methods

Demonstrates robustness and reliability in space fault detection

Highlights the advantage of physics-informed loss in space applications

Abstract

The unique challenges posed by the space environment, characterized by extreme conditions and limited accessibility, raise the need for robust and reliable techniques to identify and prevent satellite faults. Fault detection methods in the space sector are required to ensure mission success and to protect valuable assets. In this context, this paper proposes an Artificial Intelligence (AI) based fault detection methodology and evaluates its performance on ADAPT (Advanced Diagnostics and Prognostics Testbed), an Electrical Power System (EPS) dataset, crafted in laboratory by NASA. Our study focuses on the application of a physics-informed (PI) real-valued non-volume preserving (Real NVP) model for fault detection in space systems. The efficacy of this method is systematically compared against other AI approaches such as Gated Recurrent Unit (GRU) and Autoencoder-based techniques. Results show that our physics-informed approach outperforms existing methods of fault detection, demonstrating its suitability for addressing the unique challenges of satellite EPS sub-system faults. Furthermore, we unveil the competitive advantage of physics-informed loss in AI models to address specific space needs, namely robustness, reliability, and power constraints, crucial for space exploration and satellite missions.