Abstract
The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN).
Highlights
Today digitisation and Industry 4.0 significantly influence on management and operation of industrial companies
The digital transformation is bringing major changes to the industry through rising data volumes from networked production systems and the ability to analyse them. These could be opportunities for qualitative growth and shape the idea of sustainability within a company, especially if resource consumption is reduced from an overall economic perspective and environmental relief is achieved through e.g. CO2 reduction [3]
This goal is achieved by a dynamic asset evaluation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data from the considered assets and their life cycles with the help of a dynamic Bayesian network (DBN)
Summary
Today digitisation and Industry 4.0 significantly influence on management and operation of industrial companies. The digital transformation is bringing major changes to the industry through rising data volumes from networked production systems (e.g. industrial internet of things and cyper-physical production systems) and the ability to analyse them These could be opportunities for qualitative growth and shape the idea of sustainability within a company, especially if resource consumption is reduced from an overall economic perspective and environmental relief is achieved through e.g. CO2 reduction [3]. This goal is achieved by a dynamic asset evaluation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data from the considered assets and their life cycles with the help of a dynamic Bayesian network (DBN).
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