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.

翻译：本文通过分析铁路车辆正常运行期间记录的声学信号，解决轴承故障检测这一具有挑战性的问题。为此，我们采用梅尔频率倒谱系数（MFCC）作为特征，并将其输入至一个简单的多层感知机分类器。通过一项针对当前先进通勤铁路车辆的测量活动获取的真实世界数据，对所提方法进行了评估。实验表明，即使对于训练过程中未包含的轴承损伤，所选用的MFCC特征也能可靠地检测出轴承故障。