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：本文通过分析在正常运行期间记录的声音信号，解决了在机车车辆中探测轴承故障的难题。为此，我们引入了梅尔频率倒谱系数（MFCC）作为特征，这些特征用于输入一个简单的多层感知器分类器中。提出的方法在一个测量活动中使用真实数据进行评估，该数据是针对现代通勤机车车辆进行的。实验表明，选择了MFCC特征后，即使是未在训练中包含的轴承损伤也可以可靠地检测出该轴承故障。
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