Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry

TL;DR

This paper systematically reviews the latest learning paradigms and modeling methodologies used for creating Digital Twins in the process industry, highlighting current trends, challenges, and research gaps.

Contribution

It provides a comprehensive analysis of modeling approaches, paradigms, and learning strategies for Digital Twins, addressing a gap in structured literature on this topic.

Findings

Diverse modeling methodologies are employed in DT creation.

Hybrid and data-driven paradigms are prevalent in current research.

Identified challenges include data quality and integration issues.

Abstract

Central to the digital transformation of the process industry are Digital Twins (DTs), virtual replicas of physical manufacturing systems that combine sensor data with sophisticated data-based or physics-based models, or a combination thereof, to tackle a variety of industrial-relevant tasks like process monitoring, predictive control or decision support. The backbone of a DT, i.e. the concrete modelling methodologies and architectural frameworks supporting these models, are complex, diverse and evolve fast, necessitating a thorough understanding of the latest state-of-the-art methods and trends to stay on top of a highly competitive market. From a research perspective, despite the high research interest in reviewing various aspects of DTs, structured literature reports specifically focusing on unravelling the utilized learning paradigms (e.g. self-supervised learning) for DT-creation in the process industry are a novel contribution in this field. This study aims to address these gaps by (1) systematically analyzing the modelling methodologies (e.g. Convolutional Neural Network, Encoder-Decoder, Hidden Markov Model) and paradigms (e.g. data-driven, physics-based, hybrid) used for DT-creation; (2) assessing the utilized learning strategies (e.g. supervised, unsupervised, self-supervised); (3) analyzing the type of modelling task (e.g. regression, classification, clustering); and (4) identifying the challenges and research gaps, as well as, discuss potential resolutions provided.