Extended Taxonomy of Digital Twins

The Digital Twin (DT) conceptual model was initially introduced in the context of product life-cycle management by Grieves [2], and later adopted for Structural Health Monitoring (SHM) applications in various domains, including wind energy. The widespread adoption of DT-based systems can be attributed to a promise of increased accuracy of predictive capabilities and more rapid and informed decision-making [1]. In this work, we propose a classification for different types of DTs and demonstrate its value as a part of application ontology in the wind energy context. The classification is formalized and published as a taxonomy using a Simple Knowledge Organization System (SKOS) [8] data model.

Since the introduction of the DT model, a broad spectrum of DT manifestations has emerged. The term itself takes on different interpretations, depending on the context and specific use case. For example, the Aerospace Industries Association, in their position paper, proposes a general definition of a DT as a "virtual representation of a connected physical asset" along with examples and added value of 17 different DT types [1]. This is, partially, "by design," as Grieves himself indicates only two essential attributes of a DT concept: duality and strong similarity [2]. At the same time, while developing a practical DT implementation, it becomes increasingly difficult to navigate the space of DTs and select appropriate underlying algorithms, software packages, and technology stacks.

The proposed classification system is based on the differences present in the three components of a DT: the physical, virtual, and connection system. The taxonomy design follows principles such as clarity, coherence, extendibility, minimal encoding bias, and minimal ontological commitment, proposed by Gruber [3]. It builds upon existing and established taxonomies for DT components as well as established terminology for the DT types in the published literature. While prior work in this area has been conducted by [6, 7], [4], and [5], this research formalizes and evaluates the results from an ontological point of view. Importantly, this work does not aim to deliver a unified definition or claims to be a uniquely correct and static classification, but rather offers a pragmatic approach to the adoption of the DT conceptual model. The choice of the SKOS implementation allows for publishing, sharing, and linking of the proposed taxonomy, and invites further community discussion on the topic.