  In the end-of-line test of geared motors, the evaluation of product qual-ity
is important. Due to time constraints and the high diversity of variants,
acous-tic measurements are more economical than vibration measurements.
However, the acoustic data is affected by industrial disturbing noise.
Therefore, the aim of this study is to investigate the robustness of features
used for anomaly detection in geared motor end-of-line testing. A real-world
dataset with typical faults and acoustic disturbances is recorded by an
acoustic array. This includes industrial noise from the production and
systematically produced disturbances, used to compare the robustness. Overall,
it is proposed to apply features extracted from a log-envelope spectrum
together with psychoacoustic features. The anomaly de-tection is done by using
the isolation forest or the more universal bagging random miner. Most
disturbances can be circumvented, while the use of a hammer or air pressure
often causes problems. In general, these results are important for condi-tion
monitoring tasks that are based on acoustic or vibration measurements.
Fur-thermore, a real-world problem description is presented to improve common
sig-nal processing and machine learning tasks.
