Information-based Preprocessing of PLC Data for Automatic Behavior Modeling

TL;DR

This paper introduces an information-based preprocessing method for PLC data in cyber-physical systems, using statistical and spectral analysis to improve behavior modeling and reduce manual effort.

Contribution

It proposes a novel preprocessing approach combining variance, correlation, and spectral analysis to enhance data quality before modeling in manufacturing CPS.

Findings

Effective feature pruning using statistical analysis.

Sampling rate approximation aids in data analysis.

Spectral analysis reveals process periodicity.

Abstract

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).