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(2019-2)

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ENG TELEC 2NCICLE 01

ET AERO/ETT SIST 05

ET AERONÀUTICA 03

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ETT SIST TELEC 91

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GR ENG SIS TELECOMUN

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Projecte llegit

Títol: Anomaly detection of satellite systems via Machine Learning

Director: OLMOS BONAFÉ, Juan José

Departament: TSC

Títol: Anomaly detection of satellite systems via Machine Learning

Data inici oferta: 10-12-2019     Data finalització oferta: 10-07-2020


Estudis d'assignació del projecte:
    GR ENG SIST AEROESP
Tipus: Individual
 
Lloc de realització: Fora UPC    
 
        Supervisor extern: Miguel Ángel Vázquez
        Institució/Empresa: CTTC
        Titulació del Director: Enginyer Telecomunicació
 
Paraules clau:
Anomaly detection, Satellite systems, Telemetry, Machine Learning, Deep Learning, CNN, LSTM
 
Descripció del contingut i pla d'activitats:
Satellites are continuously transmitting control data to the
gateway. This data corresponds to specific payload
parameters such as orbit deviations, radio frequency signal
power values, temperature, etc. The most widely
extended approach for automatically detecting anomalous
behavior in space operations is the use of out-of-
limits (OOL) alarms. The OOL approach consists of defining an
upper and lower threshold so that when a
measurement goes above the upper limit or below the lower one,
an alarm is triggered. Then engineers will
inspect the parameter that is out of limits and determine
whether it is an anomaly or not and decide which
action to take. While out-of-limits approaches are useful and
they successfully trigger alarms when
parameter readings go out the defined thresholds, they suffer
from some limitations. First, some behaviors
are anomalous even if they are within the defined limits. It
can happen that the parameter values reach the
upper limit but do not hit it. Since engineers do not know
when this will happen they have to monitor key
telemetry parameters closely even if in most cases everything
would be nominal. Paradoxically, sometimes
the anomalous behavior is more in limits than the nominal one.
The idea of this TFG is to collaborate in the design and
development of a machine learning tool able to
preemptively detect telemetry data anomalies beyond to the
current out-of- limit approaches. Different
anomaly events will be available for study, coming from real
data operators. Eventually, a real proof of
concept will be done considering a real time connection with
the satellite teleport.
In addition to this, the analysis of traffic data will be
performed and beam congestion techniques will be
studied.
 
Overview (resum en anglès):
This project presents the development of a software that is able to detect punctual anomalies in satellite telemetry. The presented tool is a model-based method that has a modular architecture and consist in the prediction module and detection module.
The prediction module is the responsible of forecasting the telemetry given by the satellites and it is based on a deep learning techniques. Different deep learning approaches have been considered; namely, Convolutional Neural Networks and Long Short Term Memory Modules.
The detection module is in charge of labeling and infer if the data instance has abnormal behaviour or not using a dynamic threshold computed from the mean and standard deviation of a set of the prediction errors.


Data de generació 26/01/2021