Airborne-Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data

Kreuzer M, Schmidt D, Wokusch S, Kellermann W (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Conference Proceedings Title: 2023 IEEE International Conference on Prognostics and Health Management (ICPHM)

Event location: Montreal CA

URI: https://arxiv.org/abs/2304.07307

DOI: 10.1109/ICPHM57936.2023.10194026

Abstract

In this paper, we address the challenging problem of detecting bearing faults in railway vehicles by analyzing acoustic signals recorded during regular
operation. For this, we introduce Mel Frequency Cepstral Coefficients (MFCCs) as features, which form the input to a simple Multi-Layer Perceptron classifier. The proposed method is evaluated with real-world data that was obtained for state-of-the-art commuter railway vehicles in a
measurement campaign. The experiments show that with the chosen MFCC features bearing faults can be reliably detected even for bearing damages that were not included in training.

Authors with CRIS profile

Matthias Kreuzer Professur für Signalverarbeitung Walter Kellermann Professur für Signalverarbeitung

How to cite

APA:

Kreuzer, M., Schmidt, D., Wokusch, S., & Kellermann, W. (2023). Airborne-Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data. In 2023 IEEE International Conference on Prognostics and Health Management (ICPHM). Montreal, CA.

MLA:

Kreuzer, Matthias, et al. "Airborne-Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data." Proceedings of the IEEE Conference on Prognostics and Health Management (ICPHM), Montreal 2023.

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