  {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.

Audio and Speech Processing;Machine Learning;Sound