## 2 Background  
### Asset Digitalization  
This work explores the concept of asset digitalization, addressing the nuanced differences between terms such as digitization, digitalization, and digital transformation [22]. For clarity, the term \"asset digitalization\" is used here as a broad framework that encompasses both the creation of asset data and the transformative processes that unlock new value [21]. Within this context, in this paper, we define three critical dimensions that serve as the foundation for our proposed framework:  
* Asset Digitization: This foundational step involves defining an asset's data and information model, and creating a structured digital representation. Unlike existing works, which often stop at isolated models, this step establishes the basis for integrating IoT-enabled real-time data streams. * Asset Digitalization: Building on digitization, this dimension introduces workflows and dynamic updates that enhance the asset's value proposition. For instance, our framework goes beyond static representations by integrating advanced models like the Asset Health Index (AHI) to enable predictive insights and decision-making. * Digital Transformation: This broader perspective captures the societal and organizational impact of digital technologies. Unlike prior studies, this paper emphasizes how IoT integration within the asset lifecycle directly contributes to digital transformation by bridging real-time data with strategic maintenance decisions. Within the scope of asset digitalization, this paper addresses the contemporary concept of the digital twin (DT) [3; 11; 23; 24; 25; 26]. The evolving nature of digital twins poses a challenge in precisely defining the concept, with the inherent technological nature contributing to their dynamic essence. The examination of the relationship and role of digital twins in maintenance is discussed in reference [6]. This paper positions itself as a \"digital shadow\" falling between a digital model and a digital twin proposal [10]. Infrastructure assets inherently exhibit passivity, setting them apart from industrial assets capable of actively closing the control loop and adjusting operational variables based on intelligent maintenance insights fed back to the asset. In infrastructure maintenance, intelligent decision-making is reintegrated into the asset through meticulous maintenance planning and scheduling. Consequently, the outcomes presented in this paper occupy a position between a digital model and a digital twin proposal. While the work does not explicitly align itself with the broader 'umbrella' of a digital twin, as it is not the primary focus of this research, the central emphasis remains on advancing asset digitalization. Despite not explicitly categorizing the work as a DT, the foundational principles outlined are deemed valuable in complementing the broader DT concept, emphasizing a focus on asset digitalization. Moving beyond digital twins, the Asset Administration Shell (AAS) emerges as a key concept in Industry 4.0 [9; 27; 28]. The AAS serves as the embodiment of the \"digitaltwin\" concept, facilitating cross-vendor interoperability and interaction. It encompasses essential information and functionalities for digital representation, contributing to novel digital business models and use cases. Sub-models within the AAS encapsulate varied information, aligning with the vision of seamless and interoperable interactions in the industrial landscape. The ISO 23247 series [29] introduces the term OME (Observe Manufacturing Element) and defines the digital twin for manufacturing. This definition emphasizes a fit-for-purpose digital representation with synchronization between the OME and its digital counterpart. The standard defines various types of OMEs and delineates essential fundamental attributes for their management [30]. Like AAS, the importance of precisely defining the element to be modeled, establishing digital entities, and segregating functions and models is underscored.