Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F17%3A39910397" target="_blank" >RIV/00216275:25510/17:39910397 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.21595/vp.2017.18492" target="_blank" >http://dx.doi.org/10.21595/vp.2017.18492</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21595/vp.2017.18492" target="_blank" >10.21595/vp.2017.18492</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside

Popis výsledku v původním jazyce

This research examines two different methods; Wavelet Packet Energy (WPE) and Time-domain Features (TDF) which are effective in faulty signal feature extraction of metro wheels in wayside level using vibration sensors. Signals of each wheelset passing of a trainset with both healthy and faulty wheels are recorded by the vibration sensors which are mounted on both left and right rails and a novel one-period sampling is performed at 51.2 kHz sample rate. Retrieved signal samples are used in the construction of a database which is consistent of healthy and faulty cases. Since the database has insufficient number of faulty samples, the database is balanced by a method so called Adaptive Synthetic Sampling (ADASYN) so that each class has the same number of observations. Two state-of art classifiers; Support Vector Machines (SVM) and Fisher Linear Discriminant Analysis (FLDA) are employed by utilizing 16-fold cross validation to solve the two-class problem. Referring to the results, SVM-I-TDF outperforms by classifying all samples with a success rate of 100 % and other methods have also promising results. Proposed methods may be used in the condition monitoring of metro wheelsets effectively by means of not only performance but also cost-efficiency.

Název v anglickém jazyce

Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside

Popis výsledku anglicky

This research examines two different methods; Wavelet Packet Energy (WPE) and Time-domain Features (TDF) which are effective in faulty signal feature extraction of metro wheels in wayside level using vibration sensors. Signals of each wheelset passing of a trainset with both healthy and faulty wheels are recorded by the vibration sensors which are mounted on both left and right rails and a novel one-period sampling is performed at 51.2 kHz sample rate. Retrieved signal samples are used in the construction of a database which is consistent of healthy and faulty cases. Since the database has insufficient number of faulty samples, the database is balanced by a method so called Adaptive Synthetic Sampling (ADASYN) so that each class has the same number of observations. Two state-of art classifiers; Support Vector Machines (SVM) and Fisher Linear Discriminant Analysis (FLDA) are employed by utilizing 16-fold cross validation to solve the two-class problem. Referring to the results, SVM-I-TDF outperforms by classifying all samples with a success rate of 100 % and other methods have also promising results. Proposed methods may be used in the condition monitoring of metro wheelsets effectively by means of not only performance but also cost-efficiency.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20306 - Audio engineering, reliability analysis

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

Vibroengineering Procedia

ISBN

—

ISSN

2345-0533

e-ISSN

neuvedeno

Počet stran výsledku

6

Strana od-do

13-18

Název nakladatele

JVE International

Místo vydání

Kaunas

Místo konání akce

Liberec

Datum konání akce

30. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—