{In this paper, we address the challenging problem of detecting bearing faults from vibration signals. For this, several time- and frequency-domain features have been proposed already in the past. However, these features are usually evaluated on data originating from relatively simple scenarios and a significant performance loss can be observed if more realistic scenarios are considered. To overcome this, we introduce Mel-Frequency Cepstral Coefficients (MFCCs) and features extracted from the Amplitude Modulation Spectrogram (AMS) as features for the detection of bearing faults. Both AMS and MFCCs were originally introduced in the context of audio signal processing but it is demonstrated that a significantly improved classification performance can be obtained by using these features. Furthermore, to tackle the characteristic data imbalance problem in the context of bearing fault detection, i.e., typically much more data from healthy bearings than from damaged bearings is available, we propose to train a One-class \ac{SVM} with data from healthy bearings only. Bearing faults are then classified by the detection of outliers. Our approach is evaluated with data measured in a highly challenging scenario comprising a state-of-the-art commuter railway engine which is supplied by an industrial power converter and coupled to a load machine.

翻译：本文研究了从振动信号中检测轴承故障这一具有挑战性的问题。为此，过去已提出了多种时域和频域特征。然而，这些特征通常仅在相对简单场景的数据上进行评估，当面对更实际的应用场景时，会出现显著的性能下降。为解决这一问题，我们引入梅尔频率倒谱系数（MFCCs）和从幅度调制谱图（AMS）中提取的特征用于轴承故障检测。AMS和MFCCs最初均应用于音频信号处理领域，但研究表明，使用这些特征可显著提升分类性能。此外，为应对轴承故障检测中典型的数据不平衡问题（即健康轴承数据通常远多于故障轴承数据），我们提出仅使用健康轴承数据训练单类支持向量机（One-class SVM），进而通过检测异常值对轴承故障进行分类。本方法在极具挑战性的场景中进行了数据评估：该场景包括一台由工业电源变流器供电、并耦合负载电机的先进通勤铁路发动机。