Wayside Diagnosis Of Metro Wheelsets Using Acoustic Sensor Data And One-Period Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F17%3A39910395" target="_blank" >RIV/00216275:25510/17:39910395 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.engmech.cz/2017/im/doc/EM2017_proceedings_all.pdf" target="_blank" >http://www.engmech.cz/2017/im/doc/EM2017_proceedings_all.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Wayside Diagnosis Of Metro Wheelsets Using Acoustic Sensor Data And One-Period Analysis

Popis výsledku v původním jazyce

This research promises a wheelset fault diagnosis methodology for metro train sets using wayside acoustic sensors information. Throughout the research, two different feature extraction techniques; Wavelet Packet Energy (WPE) and Time-domain Features (TDF) are employed in association with two state-of-art classifiers Fisher Linear Discriminant Analysis (FLDA) and Support Vector Machines (SVMs). The database is prepared by the acquisition of wayside acoustic sensor data accompanied by optical gates that detect wheelset center position while multiple passing of a single metro train set of type 81-71M is in daily operation with the contribution of a novel approach; one-period analysis. Acquired database is then divided into two classes which represent the healthy and faulty states of the wheelsets referring to the ground truth information of a faulty wheelset. Since the faulty states are insufficient to demonstrate the real classification performance, an adaptive synthetic sampling technique (ADASYN) is utilized to increase the number of faulty states. Promising results are observed up to 93% in classification of faulty wheelsets of the metro with the proposed techniques on acoustic sensor data. This study may aid to maintenance specialists to providing a cost effective monitoring of faulty condition of metro wheelsets.

Název v anglickém jazyce

Wayside Diagnosis Of Metro Wheelsets Using Acoustic Sensor Data And One-Period Analysis

Popis výsledku anglicky

This research promises a wheelset fault diagnosis methodology for metro train sets using wayside acoustic sensors information. Throughout the research, two different feature extraction techniques; Wavelet Packet Energy (WPE) and Time-domain Features (TDF) are employed in association with two state-of-art classifiers Fisher Linear Discriminant Analysis (FLDA) and Support Vector Machines (SVMs). The database is prepared by the acquisition of wayside acoustic sensor data accompanied by optical gates that detect wheelset center position while multiple passing of a single metro train set of type 81-71M is in daily operation with the contribution of a novel approach; one-period analysis. Acquired database is then divided into two classes which represent the healthy and faulty states of the wheelsets referring to the ground truth information of a faulty wheelset. Since the faulty states are insufficient to demonstrate the real classification performance, an adaptive synthetic sampling technique (ADASYN) is utilized to increase the number of faulty states. Promising results are observed up to 93% in classification of faulty wheelsets of the metro with the proposed techniques on acoustic sensor data. This study may aid to maintenance specialists to providing a cost effective monitoring of faulty condition of metro wheelsets.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20306 - Audio engineering, reliability analysis

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Engineering Mechanics 2017 : 23rd International Conference book of fulltexts

ISBN

978-80-214-5497-2

ISSN

1805-8248

e-ISSN

neuvedeno

Počet stran výsledku

4

Strana od-do

458-461

Název nakladatele

Vysoké učení technické v Brně

Místo vydání

Brno

Místo konání akce

Svratka

Datum konání akce

15. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000411657600104