Proceedings Vol. 23 (2017)
ENGINEERING MECHANICS 2017
May 15 – 18, 2017, Svratka, Czech Republic
Copyright © 2017 Brno University of Technology, Faculty of Mechanical Engineering, Institute of Solid Mechanics, Mechatronics and Biomechanics, Brno
ISSN 1805-8248 (printed)
ISSN 1805-8256 (electronic)
list of papers scientific commitee
pages 458 - 461, full text
This research promises a wheelset fault diagnosis methodology for metro train sets using wayside acoustic sensor 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 by providing a cost effective monitoring of faulty condition of metro wheelsets.
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