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).

翻译：信息物理系统（CPS）通过提供执行器和传感器的多变量时间序列数据，为制造过程带来了巨大的优化潜力。基于自动化分析软件，可以针对时间序列数据部署自适应和响应性措施。由于现代制造的复杂性和动态性，分析和建模往往无法完全自动化。即使是机器或深度学习方法也通常依赖于先验专家知识和标注。本文提出了一种基于信息的数据预处理方法。通过应用包括方差和相关性分析在内的统计方法、事件驱动系统中采样率的近似估计以及频谱分析的应用，可以在建模之前获取关于底层制造过程的知识。本文展示了统计分析如何实现数据集中最不重要特征的剪枝，以及采样率近似方法如何为进一步的数据分析和建模奠定基础。通过利用快速傅里叶变换，可以检测来自自动化制造过程循环性质的数据底层周期性。这种基于信息的预处理方法随后将在信息物理系统的可编程逻辑控制器（PLC）的过程时间序列数据上进行验证。