Wayside Diagnosis Of Metro Wheelsets Using Acoustic Sensor Data And One-Period Analysis
The result's identifiers
Result code in 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>
Result on the web
<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
—
Alternative languages
Result language
angličtina
Original language name
Wayside Diagnosis Of Metro Wheelsets Using Acoustic Sensor Data And One-Period Analysis
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20306 - Audio engineering, reliability analysis
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
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
Engineering Mechanics 2017 : 23rd International Conference book of fulltexts
ISBN
978-80-214-5497-2
ISSN
1805-8248
e-ISSN
neuvedeno
Number of pages
4
Pages from-to
458-461
Publisher name
Vysoké učení technické v Brně
Place of publication
Brno
Event location
Svratka
Event date
May 15, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000411657600104