  Rotating machines like engines, pumps, or turbines are ubiquitous in modern
day societies. Their mechanical parts such as electrical engines, rotors, or
bearings are the major components and any failure in them may result in their
total shutdown. Anomaly detection in such critical systems is very important to
monitor the system's health. As the requirement to obtain a dataset from
rotating machines where all possible faults are explicitly labeled is difficult
to satisfy, we propose a method that focuses on the normal behavior of the
machine instead. We propose an autoencoder model-based method for condition
monitoring of rotating machines by using an anomaly detection approach. The
method learns the characteristics of a rotating machine using the normal
vibration signals to model the healthy state of the machine. A threshold-based
approach is then applied to the reconstruction error of unseen data, thus
enabling the detection of unseen anomalies. The proposed method can directly
extract the salient features from raw vibration signals and eliminate the need
for manually engineered features. We demonstrate the effectiveness of the
proposed method by employing two rotating machine datasets and the quality of
the automatically learned features is compared with a set of handcrafted
features by training an Isolation Forest model on either of these two sets.
Experimental results on two real-world datasets indicate that our proposed
solution gives promising results, achieving an average F1-score of 99.6%.
