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, p. 85-88. ISBN 978-80-227-4619-9.

Basic information

Original name

Anomaly detection for aircraft engine fault prediction

Name (in English)

Anomaly detection for aircraft engine fault prediction

Authors

RUDOLECKÝ, Tomáš (203 Czech Republic, guarantor, belonging to the institution)

Edition

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

Publisher

Nakladatel’stvo STU

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Other information

Language

Czech

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Slovakia

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

RIV identification code

RIV/00216224:14330/16:00114970

Organization unit

Faculty of Informatics

ISBN

978-80-227-4619-9

Keywords (in Czech)

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

Keywords in English

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

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Abstract

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.

In English

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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Links

MUNI/A/0945/2015, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace V. Investor: Masaryk University, Category A