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

Matthias Kreuzer, David Schmidt, Simon Wokusch, Walter Kellermann
4/14/2023
eess.AScs.LGcs.SD

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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.

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Cite this paper

@article{kreuzer2023airborne,
  title  = {Airborne Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data},
  author = {Matthias Kreuzer and David Schmidt and Simon Wokusch and Walter Kellermann},
  year   = {2023},
  eprint = {2304.07307},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.07307v2}
}

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