Consolidating Industrial Batch Process Data for Machine Learning

Abstract

The paradigm change of Industry 4.0 brings attention to data-driven modeling and the incentive to apply machine learning methods in the process industry. Further, capitalizing on a great deal of data available is an adverse task. For batch processes, the dataset is in a threeway format (Batch × Sensor × Time). Depending on the process and the goal of the analysis, it might be necessary to aggregate batches together. For this reason, a campaign unfolding structure is applied. By grouping the batches under new labels relevant to the analytical goal, campaigns are created. These labels can be created from periodical occurrences, such as refurbishing the refractory lining in the case of the case study. In order to utilize the three-way batch format, it is necessary to align the batches. In order to address this, the feature-oriented approach Statistical Pattern Analysis (SPA) is applied. SPA derives statistics, e.g., mean, skewness and kurtosis from the time series, consequently aligning the batches. The SPA and the campaign approach create a dataset consisting of select statistics instead of an irregular three-way array. Functional data analysis (FDA) is used to smooth and extract first- and second-order derivative information from the sensors in which functional behavior can be observed before creating features. Principal Component Analysis (PCA) is used to examine the final dataset. Further, industrial processes are notoriously nonlinear, and even more so batch processes. Therefore, kernel-based principal component analysis (KPCA) is used to review the final dataset. The KPCA can accommodate different underlying characteristics by modifying the kernel function used.