Kreuzer M, Schmidt D, Wokusch S, Kellermann W (2026)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2026
Book Volume: 26
Journal Issue: 6
URI: https://www.mdpi.com/1424-8220/26/6/1947
DOI: 10.3390/s26061947
Open Access Link: https://doi.org/10.3390/s26061947
In this paper, the task of detecting bearing faults in railway vehicles during regular operation by analyzing acoustic (airborne sound) data is addressed. To that end, various features are studied, among which the Mel Frequency Cepstral Coefficients (MFCCs) are best suited for detecting bearing faults by analyzing airborne sound. The MFCCs are used to train a Multi-Layer Perceptron (MLP) classifier. The proposed method is evaluated with real-world data for a state-of-the-art commuter railway vehicle in a dedicated measurement campaign. Classification results demonstrate that the chosen MFCC features allow for reliable detection of bearing damages, even for damages that were not included in training.
APA:
Kreuzer, M., Schmidt, D., Wokusch, S., & Kellermann, W. (2026). Real-World Airborne Sound Analysis for Health Monitoring of Bearings in Railway Vehicles. Sensors, 26(6). https://doi.org/10.3390/s26061947
MLA:
Kreuzer, Matthias, et al. "Real-World Airborne Sound Analysis for Health Monitoring of Bearings in Railway Vehicles." Sensors 26.6 (2026).
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