# Physics-Informed Real NVP for Satellite Power System Fault Detection

arXiv: https://arxiv.org/abs/2405.17339 · HTML: https://mlsanity.com/paper/2405.17339/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 7 of 7)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Lagrangian Duality for Constrained Deep Learning](https://mlsanity.com/paper/2001.09394/) (2020)
- [Multi-scale Attention Flow for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2205.07493/) (2022)
- [Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery](https://mlsanity.com/paper/2104.00564/) (2021)
- [Fault Detection and Identification using Bayesian Recurrent Neural Networks](https://mlsanity.com/paper/1911.04386/) (2019)

## Built on by (most cited first, 4 of 4)

- [TRADE: Transfer of Distributions between External Conditions with Normalizing Flows](https://mlsanity.com/paper/2410.19492/) (2024)
- [A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series](https://mlsanity.com/paper/2407.02861/) (2024)
- [Unsupervised Novelty Detection Methods Benchmarking with Wavelet Decomposition](https://mlsanity.com/paper/2409.07135/) (2024)
- [Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection](https://mlsanity.com/paper/2504.02015/) (2025)

## Abstract (arXiv)

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.