Wayside Condition Monitoring of Metro Wheelsets Using Vibration and Acoustic Sensors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F24%3A39921498" target="_blank" >RIV/00216275:25510/24:39921498 - isvavai.cz</a>

Result on the web

<a href="https://www.iieta.org/journals/ts/paper/10.18280/ts.410316" target="_blank" >https://www.iieta.org/journals/ts/paper/10.18280/ts.410316</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18280/ts.410316" target="_blank" >10.18280/ts.410316</a>

Result language

angličtina

Original language name

Wayside Condition Monitoring of Metro Wheelsets Using Vibration and Acoustic Sensors

Original language description

This paper presents an efficient wayside acoustic and vibration-based detection for wheelset faults on metro trains, which is crucial for the safety of the run. The proposed condition monitoring scheme includes four main steps: data acquisition, signal segmentation by one-period analysis, feature extraction; Time-Domain Features (TDF), Wavelet Packet Energy (WPE) features, and Linear Configuration Pattern Kurtograms (LCP-K), which applies a location invariant textural descriptor to Kurtogram images of the signal, and classification with state-of-art; Fisher’s Linear Discriminant Analysis (FLDA), Support Vector Machine (SVM), Decision Tree (Dec. Tree) and Linear Perceptron classifiers alongside classifier combination techniques. During the research, results are obtained on both measured and boosted data. Thus, two databases (A1 and A2), each of which consists of measured vibration and acoustic signals belonging to healthy and faulty cases of the wheelsets of Prague metros, are established. Due to a limited number of faulty instances, features are augmented with Adaptive Synthetic Sampling (ADASYN), and larger vibration and acoustic databases SA1 and SA2 are established to validate methods. Obtained results show that TDF with Dec. Tree classifier can detect wheelset faults by 100% with vibrations signals (A1), and the novel LCP-K algorithm outperforms both acoustic databases (A2 and SA2) up to 93%, and finally, WPE features via combined classifies, reaches a 100% fault detection performance. The proposed framework provides cost-effective maintenance, which can aid metro train specialists, with potential further applicability to other types of railways.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Traitement du Signal

ISSN

0765-0019

e-ISSN

1958-5608

Volume of the periodical

41

Issue of the periodical within the volume

3

Country of publishing house

CA - CANADA

Number of pages

12

Pages from-to

1271-1282

UT code for WoS article

001260365800016

EID of the result in the Scopus database

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