Manifold Learning and Clustering for Automated Phase Identification and Alignment in Data Driven Modeling of Batch Processes
Processing data that originates from uneven, multi-phase batches is a challenge in data-driven modeling.Training predictive and monitoring models requires the data to be in the right shape to be informative.Only then can a model learn meaningful features that describe the deterministic variability of the process.The presence of multiple phases in t