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

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