Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20306 - Audio engineering, reliability analysis
Result continuities
Project
<a href="/en/project/TE01020038" target="_blank" >TE01020038: Competence Center of Railway Vehicles</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Vibroengineering Procedia
ISBN
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ISSN
2345-0533
e-ISSN
neuvedeno
Number of pages
6
Pages from-to
13-18
Publisher name
JVE International
Place of publication
Kaunas
Event location
Liberec
Event date
May 30, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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