Anomaly detection for aircraft engine fault prediction

RUDOLECKÝ, Tomáš. Anomaly detection for aircraft engine fault prediction. In Mária Bieliková, Ivan Srba. Proceedings in Informatics and Information Technologies. Bratislava: WIKT & DaZ. Bratislava: Nakladatel’stvo STU, 2016, s. 85-88. ISBN 978-80-227-4619-9.

Základní údaje

Originální název

Anomaly detection for aircraft engine fault prediction

Název anglicky

Anomaly detection for aircraft engine fault prediction

Autoři

RUDOLECKÝ, Tomáš (203 Česká republika, garant, domácí)

Vydání

Bratislava, Proceedings in Informatics and Information Technologies. Bratislava: WIKT & DaZ, od s. 85-88, 4 s. 2016

Nakladatel

Nakladatel’stvo STU

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Další údaje

Jazyk

čeština

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Slovensko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/16:00114970

Organizační jednotka

Fakulta informatiky

ISBN

978-80-227-4619-9

Klíčová slova česky

predikce poruch; letadlový motor; algoritmy podpůrných vektorů; detekce anomálií v rozložení dat

Klíčová slova anglicky

fault prediction; aircraft engine; support vector machine; group anomaly detection

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Anotace

V originále

Aircraft engine failures can be expensive and an obvious security threat. When we are able to predict a potential failure of an engine in advance, then we can send the aircraft for maintenance. Sensor data is collected during engine starts, takeoffs, cruise or special events. Aim of this research is to create a model of standard behavior of so called healthy engines and based on that, detect serious change which can predicts a failure. Furthermore, we want to distinguish among particular failure types. The model don’t have just to be able to successfully pass data tests but also should have some physical explanation. Sometimes the resultant model shows big dependences on attributes which should be at most auxiliary, or it shows physically improbable relations among attributes. We present the first results obtained with One-class Support Vector Machine, which show significant increase of the anomaly factor of two out of four faulted engines when they were approaching the failure. We also made experiments with group anomaly detection.

Anglicky

Aircraft engine failures can be expensive and an obvious security threat. When we are able to predict a potential failure of an engine in advance, then we can send the aircraft for maintenance. Sensor data is collected during engine starts, takeoffs, cruise or special events. Aim of this research is to create a model of standard behavior of so called healthy engines and based on that, detect serious change which can predicts a failure. Furthermore, we want to distinguish among particular failure types. The model don’t have just to be able to successfully pass data tests but also should have some physical explanation. Sometimes the resultant model shows big dependences on attributes which should be at most auxiliary, or it shows physically improbable relations among attributes. We present the first results obtained with One-class Support Vector Machine, which show significant increase of the anomaly factor of two out of four faulted engines when they were approaching the failure. We also made experiments with group anomaly detection.

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Návaznosti

MUNI/A/0945/2015, interní kód MU

Název: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace V. Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace V., DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty