Information-based Preprocessing of PLC Data for Automatic Behavior Modeling

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).

References in corpus (22)

• A heuristic derivation of the uncertainty of the frequency determination in time series data
• A Survey on Preprocessing Methods for Web Usage Data
• Non-uniform sampled scalar diffraction calculation using non-uniform fast Fourier transform
• Detecting multiple periodicities in observational data with the multifrequency periodogram - II. Frequency Decomposer, a parallelized time-series analysis algorithm
• Detection of periodic signals in AGN red noise light curves: empirical tests on the Auto-Correlation Function and Phase Dispersion Minimization
• Detecting the periodicity of highly irregularly sampled light-curves with Gaussian processes: the case of SDSSJ025214.67-002813.7
• A new robust feature selection method using variance-based sensitivity analysis
• Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms
• Comparing the frequentist and Bayesian periodic signal detection: rates of statistical mistakes and sensitivity to priors
• Detection of large-scale noisy multi-periodic patterns with discrete double Fourier transform
• Spectral Processing of COVID-19 Time-Series Data
• PIPS, an advanced platform for period detection in time series -- I. Fourier-likelihood periodogram and application to RR Lyrae Stars
• Financial Time Series Data Processing for Machine Learning
• Fast Multipole Method based filtering of non-uniformly sampled data
• Doubly Robust Feature Selection with Mean and Variance Outlier Detection and Oracle Properties
• Correlation Based Feature Subset Selection for Multivariate Time-Series Data
• Finding Groups of Cross-Correlated Features in Bi-View Data
• Feature Correlation Aggregation: on the Path to Better Graph Neural Networks
• Automated period detection from variable stars' time series database
• Generative Models for Periodicity Detection in Noisy Signals
• Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers
• Validating CircaCP: a Generic Sleep-Wake Cycle Detection Algorithm