  The increasing deployment of low-cost industrial IoT (IIoT) sensor platforms
on industrial assets enables great opportunities for anomaly classification in
industrial plants. The performance of such a classification model depends
highly on the available training data. Models perform well when the training
data comes from the same machine. However, as soon as the machine is changed,
repaired, or put into operation in a different environment, the prediction
often fails. For this reason, we investigate whether it is feasible to have a
robust and transferable method for AI based anomaly classification using
different models and pre-processing steps on centrifugal pumps which are
dismantled and put back into operation in the same as well as in different
environments. Further, we investigate the model performance on different pumps
from the same type compared to those from the training data.
