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：本文研究了通过分析常规运行期间记录的声学信号来检测轨道交通车辆轴承故障这一具有挑战性的问题。为此，我们引入梅尔频率倒谱系数（MFCCs）作为特征，并将其输入到简单的多层感知器分类器中。所提出的方法利用测量活动中针对最先进通勤轨道交通车辆获取的实况数据进行评估。实验表明，通过选定的MFCC特征，即使对于训练中未包含的轴承损伤，也能可靠地检测出轴承故障。