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Cyber-physical systems (CPS) offer immense optimization potential for
manufacturing processes through the availability of multivariate time series
data of actors and sensors. Based on automated analysis software, the
deployment of adaptive and responsive measures is possible for time series
data. Due to the complex and dynamic nature of modern manufacturing, analysis
and modeling often cannot be entirely automated. Even machine- or deep learning
approaches often depend on a priori expert knowledge and labelling. In this
paper, an information-based data preprocessing approach is proposed. By
applying statistical methods including variance and correlation analysis, an
approximation of the sampling rate in event-based systems and the utilization
of spectral analysis, knowledge about the underlying manufacturing processes
can be gained prior to modeling. The paper presents, how statistical analysis
enables the pruning of a dataset's least important features and how the
sampling rate approximation approach sets the base for further data analysis
and modeling. The data's underlying periodicity, originating from the cyclic
nature of an automated manufacturing process, will be detected by utilizing the
fast Fourier transform. This information-based preprocessing method will then
be validated for process time series data of cyber-physical systems'
programmable logic controllers (PLC).
