Time Series Transformation into Images for Production State Recognition
Sprache der Bezeichnung:
Englisch
Original Kurzfassung:
In the industry, there is a need to automatically identify specific production states of an
injection molding process. One reason for this is the necessity to distinguish cycles in
which something was produced from those in which nothing was produced. Monitoring
the actual runtime of an injection molding machine in this way could ensure efficiency
and cost reduction.
A sensor placed on the injection molding machine records 3D acceleration sensor data
during a production process. In this thesis, time intervals of the acceleration sensor
data are transformed into images using various image transformation methods. These
resulting images are then used to train a neural network, allowing the model to assign
new time intervals to individual production states. A significant advantage of using image
transformation methods over classical feature analysis of time series is that images can
compactly represent complex relationships. Furthermore, the use of deep learning enables
the interpretation of large amounts of data in minimal time.
The goal of this thesis is, on the one hand, to determine which transformation method,
in conjunction with a neural network, is most suitable for recognizing production states
on a single machine. On the other hand, the developed model should also be capable
of recognizing production states in data from new machines. To achieve this, the search
for the best generally applicable transformation method is conducted using a total of 15
datasets representing different machine and production variants.
Forschungs-