# Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors

arXiv: https://arxiv.org/abs/2410.09126 · HTML: https://mlsanity.com/paper/2410.09126/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Multi-Scale Convolutional Neural Networks for Time Series Classification](https://mlsanity.com/paper/1603.06995/) (2016)
- [Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning](https://mlsanity.com/paper/2110.03393/) (2021)
- [Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors](https://mlsanity.com/paper/2409.17841/) (2024)
- [A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection](https://mlsanity.com/paper/2403.12864/) (2024)

## Built on by (most cited first, 2 of 2)

- [Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Development](https://mlsanity.com/paper/2507.02602/) (2025)
- [Onboard-Targeted Segmentation of Straylight in Space Camera Sensors](https://mlsanity.com/paper/2602.20709/) (2026)

## Abstract (arXiv)

Traditional anomaly detection techniques onboard satellites are based on reliable, yet limited, thresholding mechanisms which are designed to monitor univariate signals and trigger recovery actions according to specific European Cooperation for Space Standardization (ECSS) standards. However, Artificial Intelligence-based Fault Detection, Isolation and Recovery (FDIR) solutions have recently raised with the prospect to overcome the limitations of these standard methods, expanding the range of detectable failures and improving response times. This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft for the exploration of Small Solar System Bodies (SSSB), leveraging a multi-channel Convolutional Neural Network (CNN) to perform multi-target classification and independently detect faults in the sensors. Significant attention has been dedicated to ensuring the compatibility of the algorithm within the onboard FDIR system, representing a step forward to the in-orbit validation of a technology that remains experimental until its robustness is thoroughly proven. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level. The detection performances and the capability of the algorithm in reaction triggering are evaluated employing a set of custom-defined detection and system metrics, showing the outstanding performances of the algorithm in performing its FDIR task.