Novel features for the detection of bearing faults in railway vehicles

Kreuzer M, Schmidt A, Kellermann W (2021)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

Pages Range: 1-11

Event location: Washington, DC US

DOI: 10.3397/IN-2021-2537

Open Access Link: https://arxiv.org/abs/2304.08249

Abstract

In this paper, we address the challenging problem of detecting bearing faults from vibration signals.

For this, several time- and frequency-domain features have been proposed already in the past.

However, these features are usually evaluated on data originating from relatively simple scenarios

and a significant performance loss can be observed if more realistic scenarios are considered. To

overcome this, we introduce Mel Frequency Cepstral Coefficients (MFCCs) and features extracted

from the Amplitude Modulation Spectrogram (AMS) as features for the detection of bearing faults.

Both AMS and MFCCs were originally introduced in the context of audio signal processing but it

is demonstrated that a significantly improved classification performance can be obtained by using

these features. Furthermore, to tackle the characteristic data imbalance problem in the context of

bearing fault detection, i.e., typically much more data from healthy bearings than from damaged

bearings is available, we propose to train a One-class Support Vector Machine (SVM) with data from

healthy bearings only. Bearing faults are then classified by the detection of outliers. Our approach

is evaluated with data measured in a highly challenging scenario comprising a state-of-the-art

commuter railway engine which is supplied by an industrial power converter and coupled to a load

machine.

Authors with CRIS profile

Matthias Kreuzer Professur für Signalverarbeitung Alexander Schmidt Professur für Signalverarbeitung Walter Kellermann Professur für Signalverarbeitung

How to cite

APA:

Kreuzer, M., Schmidt, A., & Kellermann, W. (2021). Novel features for the detection of bearing faults in railway vehicles. In Proceedings of the Inter-Noise 2021 - The 50th International Congress and Exposition on Noise Control Engineering (pp. 1-11). Washington, DC, US.

MLA:

Kreuzer, Matthias, Alexander Schmidt, and Walter Kellermann. "Novel features for the detection of bearing faults in railway vehicles." Proceedings of the Inter-Noise 2021 - The 50th International Congress and Exposition on Noise Control Engineering, Washington, DC 2021. 1-11.

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