A Framework for Establishing Digital Twin Information Requirements and Foundation Data Models

Abstract

Digital Twins (DTs) for asset maintenance frequently underperform because information requirements are captured as unstructured data wish-lists rather than as traceable specifications linked to operational decisions. This paper presents a general, asset-agnostic methodology to establish DT information requirements for maintainable assets. The methodology combines triangulated qualitative evidence (semi-structured expert interviews and systematic document review) with process formalisation and requirements engineering. We propose a four-layer mapping that links (i) assets for maintenance, (ii) maintenance stages, (iii) influencing factors affecting each stage, and (iv) information requirements that quantify or qualify those factors. Each information requirement is specified with semantics, spatial/temporal reso lution, provenance, update cadence, and quality expectations to support operational use as well as time-stamped reasoning (forensics and prediction). The outcome is a decision traceable requirements set and a reusable representation that can be instantiated as a time-stamped, layered graph schema. We demonstrate the methodology on pavement management to illustrate how requirements are elicited, structured, and prioritised for DT-enabled workflows, without constraining the proposed method to a specific asset domain. The paper concludes with guidance on transferability to other asset types and discusses practical considerations for implementation and validation.