衛星多変量時系列における故障検知のための自己教師ありタスク
A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series
物理情報を組み込んだReal NVPニューラルネットにセンサデータの並べ替えに基づく自己教師ありタスクを追加し、衛星の多変量時系列における故障検知性能を向上させた研究。
著者: Carlo Cena, Silvia Bucci, Alessandro Balossino, Marcello Chiaberge
分類: cs.LG, cs.RO
原文アブストラクト
In the space sector, due to environmental conditions and restricted accessibility, robust fault detection methods are imperative for ensuring mission success and safeguarding valuable assets. This work proposes a novel approach leveraging Physics-Informed Real NVP neural networks, renowned for their ability to model complex and high-dimensional distributions, augmented with a self-supervised task based on sensors' data permutation. It focuses on enhancing fault detection within the satellite multivariate time series. The experiments involve various configurations, including pre-training with self-supervision, multi-task learning, and standalone self-supervised training. Results indicate significant performance improvements across all settings. In particular, employing only the self-supervised loss yields the best overall results, suggesting its efficacy in guiding the network to extract relevant features for fault detection. This study presents a promising direction for improving fault detection in space systems and warrants further exploration in other datasets and applications.
