Airborne Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data
This addresses a safety-critical maintenance problem for railway operators, but it is incremental as it applies existing methods to a specific domain.
The paper tackled bearing fault detection in railway vehicles by analyzing acoustic signals with MFCC features and an MLP classifier, achieving reliable detection even for unseen bearing damages using real-world data.
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.