  In the context of Industry 4.0, the knowledge extraction from sensor
information plays an important role. Often, information gathered from sensor
values reveals meaningful insights for production levels, such as anomalies or
machine states. In our use case, we identify production phases through the
inspection of sensor values with the help of convolutional neural networks. The
data set stems from a tempering furnace used for metal heat treating. Our
supervised learning approach unveils a promising accuracy for the chosen neural
network that was used for the detection of production phases. We consider
solutions like shown in this work as salient pillars in the field of predictive
maintenance.
