A Trustworthy Digital Twin for Data Driven Verification

Abstract Traditionally, verification of equipment and systems has been carried out by deploying surveyors to perform physical verifications. Today, the development of digital twins, representing physical assets, provides the possibility to leverage data to enhance and replace the conventional verification and validation efforts undertaken by industry stakeholders. Digitalization, new technologies, algorithms, and artificial intelligence (AI), incorporated in digital twins, can be used to execute effective verifications. When such digital twins have proven to provide genuine and trustworthy evidence, the evidence can be used in assessments towards specified acceptance criteria, and issue, maintain, and renew certificates. Some parts of the assessment itself may even become automated through the use of algorithms and AI solutions, further increasing the importance of the rigor and intensity with which these digital twins must be qualified and assured. This paper is developed in order to support the development of trustworthy digital twins for data-driven-verification (DDV) systems and methods. Use of digital twins should be acceptable as long as they provide the same, or higher, level of assurance as the traditional methods. DDV also aims at reducing non-productive time, costs associated with surveyors attending the physical asset, redundant verification activities, and the knowledge sharing, and transfer burden imposed upon the asset organization.