  Fault detection at rotating machinery with the help of vibration sensors
offers the possibility to detect damage to machines at an early stage and to
prevent production downtimes by taking appropriate measures. The analysis of
the vibration data using methods of machine learning promises a significant
reduction in the associated analysis effort and a further improvement in
diagnostic accuracy. Here we publish a dataset which is used as a basis for the
development and evaluation of algorithms for unbalance detection. For this
purpose, unbalances of various sizes were attached to a rotating shaft using a
3D-printed holder. In a speed range from approx. 630 RPM to 2330 RPM, three
sensors were used to record vibrations on the rotating shaft at a sampling rate
of 4096 values per second. A development and an evaluation dataset are
available for each unbalance strength. Using the dataset recorded in this way,
fully connected and convolutional neural networks, Hidden Markov Models and
Random Forest classifications on the basis of automatically extracted time
series features were tested. With a prediction accuracy of 98.6 % on the
evaluation dataset, the best result could be achieved with a fully-connected
neural network that receives the scaled FFT-transformed vibration data as
input.
