Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside
Konferenční objektOtevřený přístuppeer-reviewedpostprintSoubory
Datum publikování
2017
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JVE International
Abstrakt
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.
Rozsah stran
p. 13-18
ISSN
2345-0533
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Zdrojový dokument
Vibroengineering Procedia
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open access
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25th International Conference on Vibroengineering (30.05.2017 - 01.06.2017, Liberec)
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wavelet packet energy, time domain features, wheelset fault diagnosis, one period analysis, Fisher linear discriminant analysis, support vector machine, wavelet packet energy, time domain features, diagnostika vad dvojkolí, analýza jedné otáčky, Fisherova lineární diskriminační analýza, support vector machine