Dimensionality reduction for visualizing industrial chemical process data

doi:10.1016/j.conengprac.2019.104189

Dimensionality reduction for visualizing industrial chemical process data

2019-12-01

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DOI：10.1016/j.conengprac.2019.104189

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This paper explores dimensionality reduction (DR) approaches for visualizing high dimensional data in chemical processes. Visualization provides powerful insight and process understanding in the industrial context, and accelerates process troubleshooting. A diverse array of existing, easy-to-use DR methods are evaluated in three case studies on large-scale industrial manufacturing plants. Supervised and unsupervised cases are presented with the objective of solving typical industrial problems related to unplanned events, plant performance improvement, and quality underperformance troubleshooting. For the unsupervised case, the evaluation aims to identify approaches that provide insight beyond those of PCA (Principal Component Analysis), and also examines quality metrics of the reduced (latent) space which characterize the degree of trust in the DR. UMAP (Uniform Manifold Approximation and Projection) outperforms other techniques, bringing new insights when comparing with other methods. For the supervised case, UMAP is combined with traditional variable selection methods, such as VIP (Variable Influence on Projection) weights from PLS-DA (Partial Least Squares Discriminant Analysis), in order to improve latent space visualization by increasing separation between classes.

Keywords:

UMAP

Unsupervised learning

Supervised learning

Feature selection

Manufacturing analytics

Classification

Dimensionality reduction for visualizing industrial chemical process data

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