Wayside Diagnosis of Wheelset Faults of Metros using One-period Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F17%3A39910857" target="_blank" >RIV/00216275:25510/17:39910857 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Wayside Diagnosis of Wheelset Faults of Metros using One-period Analysis

Popis výsledku v původním jazyce

This research is focused on detection of wheelset faults of Prague metro train set of type 81-71M using vibration sensors on the wayside with the contribution of a novel one period analysis. Vibration sensor activities for each metro passing are recorded by two accelerometer sensors which are mounted on both right and left rail for all day while metros are in routine operation. Signal samples of two known faulty wheels of a wheelset on ID-108 metro are used as ground truth information in comparison to ID-119 healthy data. The database has 16 faulty signal samples against 16 healthy ones. Three different methodologies; Wavelet Packet Energy (WPE), Time-Domain Features (TDF) and Linear Configuration Pattern Kurtograms (LCP-K) are used with Fisher Linear Discriminant Analysis and Support Vector Machines classifiers. Outstanding results are observed among proposed techniques up to 100%. Proposed methods may be used for a cost-effective wayside diagnostic system for railway vehicles.

Název v anglickém jazyce

Wayside Diagnosis of Wheelset Faults of Metros using One-period Analysis

Popis výsledku anglicky

This research is focused on detection of wheelset faults of Prague metro train set of type 81-71M using vibration sensors on the wayside with the contribution of a novel one period analysis. Vibration sensor activities for each metro passing are recorded by two accelerometer sensors which are mounted on both right and left rail for all day while metros are in routine operation. Signal samples of two known faulty wheels of a wheelset on ID-108 metro are used as ground truth information in comparison to ID-119 healthy data. The database has 16 faulty signal samples against 16 healthy ones. Three different methodologies; Wavelet Packet Energy (WPE), Time-Domain Features (TDF) and Linear Configuration Pattern Kurtograms (LCP-K) are used with Fisher Linear Discriminant Analysis and Support Vector Machines classifiers. Outstanding results are observed among proposed techniques up to 100%. Proposed methods may be used for a cost-effective wayside diagnostic system for railway vehicles.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20104 - Transport engineering

Projekt

<a href="/cs/project/TE01020038" target="_blank" >TE01020038: Centrum kompetence drážních vozidel</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Symposium on Electrical Railway Transportation Systems : symposium proceedings

ISBN

978-605-01-1072-2

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

4

Strana od-do

23-26

Název nakladatele

The Chamber of Electrical Engineers, Eskişehir branch

Místo vydání

Eskişehir

Místo konání akce

Eskişehir

Datum konání akce

27. 10. 2017

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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