On-Board Telemetry Monitoring via Support Vector Machine with Application to Philae Solar Generator

Aerotecnica Missili & Spazio

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Title On-Board Telemetry Monitoring via Support Vector Machine with Application to Philae Solar Generator
Creator Infantolino, Gabriele Martino
Di Lizia, Pierluigi
Topputo, Francesco
Bernelli Zazzera, Franco
Subject Space Systems
Philae; SVM; Classification; Fault; Solar; Array

Description Recent developments in machine learning for anomaly detection make it possible to use spacecraft status telemetry to produce sophisticated system health monitoring applications, that can run autonomously on-board. Both archived and simulated data are used to train intelligent algorithms to automatically detect and classify anomalous time series of the produced telemetry. The focus of this work is the application of a Support Vector Machine (SVM) based classier to monitor the status of an interplanetary probe photovoltaic system with a minimum set of available measurements. SVM is a popular machine learning method for classication and regression and it has outstanding generalization performance.The Rosetta lander Philae is considered as test case. A complete model of power production subsystem has been developed to simulate the real telemetry. Training data, generated for nominal and faulty cases, are used to train the SVM model, with the goal of classifying permanent and temporary power loss conditions. The simulated telemetry is then used to test its performance and to identify the minimum number of  measurements that is necessary for a successful classication of the failures of interest.
Publisher Associazione Italiana di Aeronautica e Astronautica (AIDAA)
Date 2019-01-24
Type info:eu-repo/semantics/article
Peer-reviewed Article
Numerical; Simulation
Format application/pdf
Source Aerotecnica Missili & Spazio; Vol 97, No 4 (2018); 183-188
Language eng
Rights Copyright (c) 2019 Aerotecnica Missili & Spazio

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