Process Data Analytics via Probabilistic Latent Variable Models: A Tutorial Review

Dimensionality reduction is important for the high-dimensional nature of data in the process industry, which has made latent variable modeling methods popular in recent years. By projecting high-dimensional data into a lower-dimensional space, latent variables models are able to extract key information from process data while simultaneously improving the efficiency of data analytics. Through a probabilistic viewpoint, this paper carries out a tutorial review of probabilistic latent variable models on process data analytics. Detailed illustrations of different kinds of basic probabilistic latent variable models (PLVM) are provided, as well as their research statuses. Additionally, more counterparts of those basic PLVMs are introduced and discussed for process data analytics. Several perspectives are highlighted for future research on this topic.