Asset Digitalization Strategy using IoT Platforms and Asset Health Model

IFAC PapersOnLine 58-8 (2024) 216–221 ScienceDirect Available online at www.sciencedirect.com 2405-8963 Copyright © 2024 The Authors. This is an open access article under the CC BY-NC-ND license . Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2024.08.123 Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. 10.1016/j.ifacol.2024.08.123 2405-8963 Copyright © 2024 The Authors. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ) Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: ajg[email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: j[email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. E. Candón et al. / IFAC PapersOnLine 58-8 (2024) 216–221 217 Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. Copyright © 2024 The Authors. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ) Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. Asset Digitalization Strategy using IoT Platforms and Asset Health Model Candón, E.*, Crespo, A.**, Guillén, A.***, Gómez, J. ****, López, J. ***** * Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) *** Department of Management, Complutense University of Madrid, Spain (e-mail: [email protected]) **** Department of Industrial Management, University of Seville, Spain (e-mail: [email protected]) ***** Technology and Innovation Director of CEN Solutions, Seville, Spain (e-mail: [email protected]) Abstract: Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization. This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability. This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures Keywords: Asset digitalization, Digital Maintenance, IoT, Asset health indexing, Civil infrastructure digitalization, Bridge Maintenance. 1. INTRODUCTION The digital revolution in Industry 5.0 demands a redefinition of assets. The fusion of physical and digital dimensions with the human dimension creates complex systems of systems (SoS) (Compare et al. 2017; Wang et al. 2022; Ordieres-Meré et al. 2023). Global interoperability, beyond technological challenges, requires an advanced perspective on assets, processes, and human involvement. In the battle against interoperability challenges, the effective integration of data into each asset is crucial. This surpasses information silos, where relevant data is scattered across systems, applications, devices, and cloud platforms. This dispersion limits accessibility and coherence, hindering smooth interaction with stakeholders. The digital dimension of each asset includes raw data and key elements such as data, models, and services, essential for Digital Twins (Lin, 2022). As digitization progresses beyond its initial stage, which primarily focused on data acquisition and the creation of isolated models, it has become crucial to pay attention to the management of these models. They play a fundamental role in facilitating the development of innovative services that enhance the value proposition of assets, with the ultimate goal of significantly improving decision-making processes. Currently, advanced platforms favor accessibility and data processing, enabling the development of models to digitize asset management and maintenance. However, this high potential of models is not fully utilized in the management of critical infrastructures. While there are maintenance management frameworks and references for IT systems in the 4.0 context, fully integrated solutions combining both approaches are not well-stablished. Technology and platform design play a crucial role in the digitalization landscape, but there's a pressing need to shift towards asset-centric approaches. The maturation of IoT technology, particularly in recent years, positions it as a practical gateway for asset digitalization. IoT networks act as the bridge connecting physical assets with digital platforms, as seen in reference architectures like IIRA (Lin, 2022) or RAMI 4.0 (Plattform Industrie 4.0 - Whitepaper “Modelling the Semantics of Data of an Asset Administration Shell with Elements of ECLASS”), where IoT sensor networks are part of the physical asset layer. Designing an IoT network requires identifying and describing monitored assets, but this initial digital treatment falls short in supporting advanced asset management digitalization and decision-making processes. The paper considers how a model-based approach to asset digitization can be effectively introduced in the service of maintenance and asset management, and how this approach can be integrated into maintenance decision-making processes. 2. BACKGROUND 2.1 Asset Digitalization This paper delves into the concept of asset digitalization. While acknowledging the complexities of terminology, particularly digitization, digitalization, and digital transformation (Raza et al. 2023), this work adopts the term "asset digitalization" as a comprehensive umbrella encompassing both the design of asset data and the transformative processes driving new value creation (Gong and Ribiere 2020). In alignment with this perspective, the paper contributes to three key aspects: - Asset Digitization: This involves defining the foundational data and information model of an asset, creating a digital counterpart with the requisite level of detail. - Asset Digitalization: Beyond mere digitization, this encompasses the generation of new processes, initiating with the definition and continuous updating of the digital counterpart. The focus lies on expanding the asset's value proposition and management through emerging processes, not necessarily reliant on digital technologies at their current level of handling. - Digital Transformation: The paper's impact on business and society positions it as a contributor to digital transformation, considering the broader vision of enterprise transformation and its role in shaping the digital society. 2.2 Digital Maintenance The digitization of maintenance, encompassed by the broader concept of asset digitalization, plays a pivotal role in propelling digital transformation. This assertion is substantiated by the direct application of a significant portion of IoT platforms and PHM techniques to maintenance (Guillén et al. 2016; Compare et al. 2017; Errandonea et al. 2020; Marquez et al. 2020) where real-time asset degradation information is generated through massive data collection. The digitalization of assets requires a technical design within a designated IT architecture for integrating different systems and platforms. It's essential to note that digitalization solutions often emerge from a technological standpoint. However, a maintenance-centered approach is crucial to supplement the technology-focused perspective. This approach ensures the longevity and reliability of digitalized assets aligned with overarching business objectives. It involves comprehending and managing the digital functionalities of the IT solution, as well as supervising end-to-end processes for handling data and models, all focused on maintenance requirements, without delving into architecture design specifics. Considering the role of end users, providers of information systems strategically design their technical and commercial offerings centered around platforms and apps. A platform integrates essential digital services to provide technical support throughout end-to-end processes, closely resembling the IoT and cloud platform conception. Apps are linked to core functionality desired by users, requiring a platform for seamless integration and operation in today’s complex industrial systems. Intelligent Assets Management Platforms (IAMP) collect and analyze data from industrial assets, commonly falling into categories like Enterprise Asset Management (EAM), Asset Performance Management (APM), and Asset Investment Planning (AIP). In this context, a referenced visual tool (Figure 1) is suggested by (Crespo Márquez 2022) to integrate the Input-Process- Output (I-P-O) schema with the necessity of employing multiple models and apps throughout various phases of the comprehensive maintenance process. This representation involves transforming raw data from diverse business systems into specific models essential for various aspects of asset digitalization. The I-P-O process aligns with the ISO 14224:2016 framework, with each block corresponding to a function (e.g., ETL, Database, IAM, AI, and BI) to ensure a streamlined approach to digital maintenance management. These functions utilize models to generate outputs associated with various IAM Apps, supporting tasks linked to each phase of the maintenance and asset management process. Figure 1. Schema of digital maintenance framework proposed by (Crespo Márquez 2022) 218 E. Candón et al. / IFAC PapersOnLine 58-8 (2024) 216–221 The diagram in Figure 1 presents a concept that not only identifies applicable apps across the comprehensive management scope, spanning from strategic to operational aspects, but also makes a clear distinction between apps and IT functional blocks. This proposal is grounded in the framework for asset and maintenance management initially introduced in reference (Crespo Márquez et al. 2009). This framework, widely applied in the management of network utilities facilitated by information technology systems (Gómez Fernández and Crespo Márquez 2012; Serra et al. 2019) has evolved to provide support for both digital maintenance and the design of digital twins (Crespo Márquez et al. 2020; Crespo Márquez 2022). 2.3 Reference Architecture ISO/IEC/IEEE 42010:2011, titled "Systems and software engineering - Architecture description," broadly defines architecture as "the fundamental structure of a system, or more specifically, the structure of system components, their relationships, and the principles and guidelines that govern their design and evolution over time" (ISO/IEC/IEEE 42010:2022 - Software, systems and enterprise — Architecture description). In the realm of systems engineering, architecture is indispensable for providing a framework to describe the interactions and organization of system components to achieve specific objectives. This work primarily draws on RAMI 4.0 and IIRA (Figure 2) as main references due to their significant impact and widespread acceptance in academia and industry. Both architectures share core principles and approaches essential for addressing the challenges of digitization in modern industry. They utilize the concept of layers to structure systems, enabling modularity, flexibility, and incremental updates. RAMI 4.0 introduces two axes—lifecycle and hierarchy in system integration—while IIRA provides a more simplified vision. The introduction of Autonomous Asset Services (AAS) in RAMI 4.0 is a notable contribution, playing a fundamental role in the digital transformation of the industry, as discussed in the digital twin’s section. Moreover, RAMI 4.0 explicitly incorporates the reference to the lifecycle of assets and systems, a key aspect requiring proper handling in the digitization process. 3. ASSET DIGITALIZATION USE CASE Bridges, critical in transportation infrastructures, present challenges in Total Expenditure (TOTEX) management. Controlling bridge condition is crucial for holistic management, impacting both individual bridges and the entire asset portfolio. Current tools for effective infrastructure management are limited, and climate change adds urgency to control demands, creating uncertainty in evolving conditions. Most bridges lack a real-time online monitoring system. Affordable IoT networks enable widespread monitoring, generating extensive data requiring advanced analytics for meaningful insights. Thorough data interpretation enhances understanding and issue identification. Integrating data from multiple bridges offers a global perspective, aiding in predictive model development. Due to a low initial level of digitization of bridges, during their operation and maintenance (O&M) phase in the middle period of their life cycle, this case study aims to answer the following research questions: - How can a model-based approach effectively digitalize bridges for maintenance and management? - How can the combined use of an IoT platform, short-term monitoring models, and long-term models like AHI enhance bridge digitalization and management? The asset digitization process introduces a set of four models addressing different aspects of asset digitization and management for bridges. Firstly, the Asset Definition Model offers a comprehensive framework capturing the complexities of bridge data in various systems. Secondly, the Asset Criticality Model prioritizes bridge assets based on their criticality. Thirdly, the Bridge Monitoring Model demonstrates how IoT networks and signal integration enable real-time monitoring. Lastly, Intelligent Asset Management Models are designed to estimate the health index of bridges. 3.1 The Asset Definition Model This model describes the comprehensive asset data/information which is implicitly present within the diverse information systems and apps currently utilized for the asset management. Takes the IEC 81346-1:2022 standard and ISO 14224:2016 as guidelines for this purpose. The proposed asset definition incorporates four distinct aspects, or specific ways of viewing an object: - Physical Asset Registration Code. A physical asset code refers to a tangible object or entity that can be touched, seen, and measured. - Asset Class. Connected to the assets’ technology and to the definition of technical aspects unique to the asset, regardless of its final functional location. Subclasses (or types as per the IEC 81346 standard) can be utilized to detail groups within a class, establishing distinctions within the same class. - Asset Functional Location. Refers to a specific physical or logical location where an asset is installed or used. - Asset Reference System. The choice of a coordinate reference system for an infrastructure system or network is influenced by factors such as location, precision requirements, and available resources. Figure 2. Layered reference architectures: a) IIRA; b) RAMI 4.0 E. Candón et al. / IFAC PapersOnLine 58-8 (2024) 216–221 219 The diagram in Figure 1 presents a concept that not only identifies applicable apps across the comprehensive management scope, spanning from strategic to operational aspects, but also makes a clear distinction between apps and IT functional blocks. This proposal is grounded in the framework for asset and maintenance management initially introduced in reference (Crespo Márquez et al. 2009). This framework, widely applied in the management of network utilities facilitated by information technology systems (Gómez Fernández and Crespo Márquez 2012; Serra et al. 2019) has evolved to provide support for both digital maintenance and the design of digital twins (Crespo Márquez et al. 2020; Crespo Márquez 2022). 2.3 Reference Architecture ISO/IEC/IEEE 42010:2011, titled "Systems and software engineering - Architecture description," broadly defines architecture as "the fundamental structure of a system, or more specifically, the structure of system components, their relationships, and the principles and guidelines that govern their design and evolution over time" (ISO/IEC/IEEE 42010:2022 - Software, systems and enterprise — Architecture description). In the realm of systems engineering, architecture is indispensable for providing a framework to describe the interactions and organization of system components to achieve specific objectives. This work primarily draws on RAMI 4.0 and IIRA (Figure 2) as main references due to their significant impact and widespread acceptance in academia and industry. Both architectures share core principles and approaches essential for addressing the challenges of digitization in modern industry. They utilize the concept of layers to structure systems, enabling modularity, flexibility, and incremental updates. RAMI 4.0 introduces two axes—lifecycle and hierarchy in system integration—while IIRA provides a more simplified vision. The introduction of Autonomous Asset Services (AAS) in RAMI 4.0 is a notable contribution, playing a fundamental role in the digital transformation of the industry, as discussed in the digital twin’s section. Moreover, RAMI 4.0 explicitly incorporates the reference to the lifecycle of assets and systems, a key aspect requiring proper handling in the digitization process. 3. ASSET DIGITALIZATION USE CASE Bridges, critical in transportation infrastructures, present challenges in Total Expenditure (TOTEX) management. Controlling bridge condition is crucial for holistic management, impacting both individual bridges and the entire asset portfolio. Current tools for effective infrastructure management are limited, and climate change adds urgency to control demands, creating uncertainty in evolving conditions. Most bridges lack a real-time online monitoring system. Affordable IoT networks enable widespread monitoring, generating extensive data requiring advanced analytics for meaningful insights. Thorough data interpretation enhances understanding and issue identification. Integrating data from multiple bridges offers a global perspective, aiding in predictive model development. Due to a low initial level of digitization of bridges, during their operation and maintenance (O&M) phase in the middle period of their life cycle, this case study aims to answer the following research questions: - How can a model-based approach effectively digitalize bridges for maintenance and management? - How can the combined use of an IoT platform, short-term monitoring models, and long-term models like AHI enhance bridge digitalization and management? The asset digitization process introduces a set of four models addressing different aspects of asset digitization and management for bridges. Firstly, the Asset Definition Model offers a comprehensive framework capturing the complexities of bridge data in various systems. Secondly, the Asset Criticality Model prioritizes bridge assets based on their criticality. Thirdly, the Bridge Monitoring Model demonstrates how IoT networks and signal integration enable real-time monitoring. Lastly, Intelligent Asset Management Models are designed to estimate the health index of bridges. 3.1 The Asset Definition Model This model describes the comprehensive asset data/information which is implicitly present within the diverse information systems and apps currently utilized for the asset management. Takes the IEC 81346-1:2022 standard and ISO 14224:2016 as guidelines for this purpose. The proposed asset definition incorporates four distinct aspects, or specific ways of viewing an object: - Physical Asset Registration Code. A physical asset code refers to a tangible object or entity that can be touched, seen, and measured. - Asset Class. Connected to the assets’ technology and to the definition of technical aspects unique to the asset, regardless of its final functional location. Subclasses (or types as per the IEC 81346 standard) can be utilized to detail groups within a class, establishing distinctions within the same class. - Asset Functional Location. Refers to a specific physical or logical location where an asset is installed or used. - Asset Reference System. The choice of a coordinate reference system for an infrastructure system or network is influenced by factors such as location, precision requirements, and available resources. Figure 2. Layered reference architectures: a) IIRA; b) RAMI 4.0 According to the four dimensions necessary to establish the asset definition model, the bridge under study has a specific physical asset registration code, a particular asset class and subclass, is located in a specific functional location and geolocated under an established reference system. 3.2 The Asset Criticality Model An asset criticality model is pivotal in infrastructure management, enabling the prioritization of assets based on their criticality and importance within the organization. This analysis, crucial for risk reduction, must be meticulously conducted, considering factors such as large-scale applicability, consistency with the scope, and compatibility with changes and the asset management system (Jinzhi et al. 2022). Once the criticality of assets is determined, the framework facilitates integration with other models. This includes the model for establishing a preventive maintenance plan, specifying tasks and their frequency (using methods like RCM, MTA, RCBA, etc.). It also connects with the asset monitoring model, particularly recommended for critical assets with significant impacts on infrastructure effectiveness, efficiency, and performance. Additionally, it links with models for establishing optimal scheduling and resource allocation for a maintenance plan, as well as the model for examining assets to study their degradation and life cycle cost, encompassing the asset degradation and life cycle cost models. Digitalizing bridges allows for more effective management, real-time monitoring, and a comprehensive assessment of their condition, which is essential to ensure their performance, safety, and durability over time. Additionally, the risk associated with the functional failure of a bridge not only impacts infrastructure but can also have serious implications for public safety and health, underscoring the urgency of implementing digitalization models to mitigate this risk. 3.3 Bridge Monitoring Model The digital transformation of an asset, which requires or benefits from state monitoring, is achieved by linking it to data derived from the physical world through custom monitoring solutions for each asset. Developing an asset monitoring model is a complex process involving signal integration and the establishment of a comprehensive Extract, Transform, Load (ETL) process, facilitating the conversion of signals into valuable asset-related information. Advanced systems execute this transformation by deploying sensors through an Internet of Things (IoT) network. This network consists of sensors, nodes, and cloud computing. Nodes play a crucial role in grouping sensors, ensuring effective communication between nodes, and transmitting network data to cloud platforms and applications. The proposed design is based on an IoT framework that incorporates the layer of acquisition and local processing at the measurement points (sensor nodes), the information transport network, and the processing server responsible for hosting essential services for the processing, exploitation, and presentation of information to end-users (Figure 3). To digitize the monitoring, an integration with the information coming from the IoT/Cloud network strategically located at various points of the bridge is established. The use case solution integrates both hardware and firmware design to fulfill local processing, device control, wireless communication, data storage, power supply, sensors for realtime structure monitoring, real-time operating system tasks, along with sensor monitoring tasks, local information processing, and wireless control. The server plays a crucial role by centralizing the processing and management of monitored structures. To adapt to scalability, a microservices-based development is adopted, performing tasks such as gathering information from deployed nodes, storing it in a database, and processing data according to different asset digitization models. 3.4 The Intelligent Asset Management Model Once the available information and data from the asset are captured, processed, and stored, the critical asset is appropriately structured and the operating and maintenance conditions are monitored, there arises the need to connect data models with human knowledge and reasoning to digitize the processes supporting decision-making. This digitization is achievable through intelligent asset management models, which are embedded in the Intelligent Asset Management Platform App (IAMP App) (Marquez et al. 2020). In the proposed framework in Figure 1 (Crespo Márquez 2022), various decision-making processes are outlined, such as: Root Cause Analysis (RCA); Reliability-Centered Maintenance (RCM) or Maintenance Task Analysis (MTA); Condition Based Maintenance (CBM); Maintenance Resources Optimization models (MRO); Reliability, Availability, Maintainability and Safety Analysis (RAMS); Asset Health Indexing (AHI); Life Cycle Costing (LCC); Etc. Within the IAMP App, these intelligent asset management models are configured. Through interaction with simulation tools or artificial intelligence, they enable personnel responsible for the operation and/or maintenance of the asset to make short-term, medium-term, and long-term decisions. In the proposed use case outlined in the paper for the digitization of the bridge, the ETL processes, the DB processes, and the Intelligent Asset Management Systems are integrated, supported by mathematical modelling, simulation modelling (Simulation App), and facilitating the representation of results in business intelligence tools (BI Figure 3. The IoT Network 220 E. Candón et al. / IFAC PapersOnLine 58-8 (2024) 216–221 App/User Interface). This integration of the different processes, as represented in the Figure 4, allows determining: - The health status of the bridge by calculating the Asset Health Index (AHI). This health index enables short and medium-term decision-making regarding which operational and/or maintenance activities to undertake based on the asset's condition. - The projection of AHI using simulation tools, allowing for medium and long-term decision-making. For example, through the comparison of different scenarios with possible strategies for conducting an overhaul or major maintenance of the analyzed asset. - The estimation of the Asset's Life Cycle Cost (LCC), based on past and projected operations and maintenance activities until the end of its useful life. The integration with the IoT network will allow processing monitoring data from the platform and, consequently, the calculation of the input indicators for the proposed health index model. The robustness of the proposed model allows flexible validation and recalibration of the processes defined in the model to closely represent the reality of the asset and refine decision-making. As an example obtained in the case study, the health index is calculated for four bridges of different classes, in different technical locations, and with varying operating and maintenance conditions. The results for each bridge are represented in Figure 5, where the x-axis represents age (operating time), the y-axis represents the obtained maintenance index, and the diameter of the ball represents the deviation from the predicted deterioration for each bridge. This means the difference between the health index compared to the initially predicted health index, without considering the health and reliability modifiers for the health index calculation. It is observed that Bridge 1 has the most unfavorable health index, even though it has the least number of operating hours. Despite this, as indicated by the ball diameter, this aging rate aligns quite closely with what was expected, unlike Bridge 3, which, with higher age, was expected to have a more favorable health status. This could be attributed to: - Experiencing worse operating and/or maintenance conditions than initially planned, such as increased heavy vehicle traffic beyond the initial consideration or exposure to more adverse weather conditions than those anticipated in the bridge design. To determine these causes, data mining or establishing KPIs will be necessary to identify the reasons for these deviations in the health index. - The calculated health index does not align with the actual health status of the bridge, and therefore, recalibrating the model based on the knowledge of expert personnel from the installation will be necessary. Figure 4. The AHI model presentation using the DMM framework. Figure 5. AHI versus age projection. Representation of current health index versus the initially estimated for bridge age. E. Candón et al. / IFAC PapersOnLine 58-8 (2024) 216–221 221 App/User Interface). This integration of the different processes, as represented in the Figure 4, allows determining: - The health status of the bridge by calculating the Asset Health Index (AHI). This health index enables short and medium-term decision-making regarding which operational and/or maintenance activities to undertake based on the asset's condition. - The projection of AHI using simulation tools, allowing for medium and long-term decision-making. For example, through the comparison of different scenarios with possible strategies for conducting an overhaul or major maintenance of the analyzed asset. - The estimation of the Asset's Life Cycle Cost (LCC), based on past and projected operations and maintenance activities until the end of its useful life. The integration with the IoT network will allow processing monitoring data from the platform and, consequently, the calculation of the input indicators for the proposed health index model. The robustness of the proposed model allows flexible validation and recalibration of the processes defined in the model to closely represent the reality of the asset and refine decision-making. As an example obtained in the case study, the health index is calculated for four bridges of different classes, in different technical locations, and with varying operating and maintenance conditions. The results for each bridge are represented in Figure 5, where the x-axis represents age (operating time), the y-axis represents the obtained maintenance index, and the diameter of the ball represents the deviation from the predicted deterioration for each bridge. This means the difference between the health index compared to the initially predicted health index, without considering the health and reliability modifiers for the health index calculation. It is observed that Bridge 1 has the most unfavorable health index, even though it has the least number of operating hours. Despite this, as indicated by the ball diameter, this aging rate aligns quite closely with what was expected, unlike Bridge 3, which, with higher age, was expected to have a more favorable health status. This could be attributed to: - Experiencing worse operating and/or maintenance conditions than initially planned, such as increased heavy vehicle traffic beyond the initial consideration or exposure to more adverse weather conditions than those anticipated in the bridge design. To determine these causes, data mining or establishing KPIs will be necessary to identify the reasons for these deviations in the health index. - The calculated health index does not align with the actual health status of the bridge, and therefore, recalibrating the model based on the knowledge of expert personnel from the installation will be necessary. Figure 4. The AHI model presentation using the DMM framework. Figure 5. AHI versus age projection. Representation of current health index versus the initially estimated for bridge age. 6. CONCLUSIONS This research delves into the crucial facets of maintenance digitization, acknowledging the existing gap in its effective implementation despite the strides in IoT platforms and digital twin technologies. The proposed framework not only aligns with the creation of the Asset Administration Shell (AAS) and its digital twin but also underscores a model-centric approach to asset and maintenance management in the digitization process. The research acknowledges the challenges posed by infrastructure assets lacking historical monitoring records and proposes a solution enabling the integration of monitoring variables with the characterization of technical aspects affecting comprehensive asset health degradation. The model's adaptability allows for refinement based on actual degradation processes, encouraging meticulous recording and interpretation of significant data and events. The limitations of the proposed methodology will depend on the availability of information about the physical assets, specific knowledge about their behavior, and the ability to accurately approximate their health status. However, the flexibility of the model will allow for easy recalibration and adjustment of the asset health calculation, enabling representation through successive adjustments until replicating expert knowledge about the physical asset is achieved. In summary, this study furnishes a framework for the effective digitization of maintenance, emphasizing practical implementation, model-based perspectives, and the strategic integration of an asset health index model, ultimately contributing to the advancement of digitization practices in the realm of maintenance management. 7. 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