PhD thesis
Title:
Consistency Matters: Building Consistent Digital Twin Virtual Entities
Abstract:
The concept of Digital Twin (DT) encapsulates a real-world entity (RE) and one or more virtual entities (VEs) bidirectionally connected to this RE. These VEs mimic certain aspects of the RE to facilitate various use-cases, such as predictive maintenance and optimization. DTs typically encompass various models, often developed by experts from different domains using diverse tools. To maintain consistency among these models and ensure the continued functioning of the system, effective identification and resolution of any consistency issues are imperative. In this thesis, we study consistency management in the context of DTs and develop a framework to address this challenge. Based on our interview study with 19 DT researchers and practitioners, we discovered that various consistency issues are often encountered during the development and maintenance of DTs. There exist tools and methods for addressing these issues. However, these are limited in terms of capability and applicability. Therefore, the topic of consistency management remains largely unexplored in the context of DTs. In this thesis, we address this gap by developing a consistency management framework focused on managing the consistency of DT models, specifically the ones contained within VEs. To develop the framework, we first perform a detailed analysis of the characteristics of DT models, identified and categorized through a review of the current literature. Based on these characteristics, we elicit a set of essential requirements that must be fulfilled by any system aiming to manage the consistency of DT models. We evaluate our consistency management framework by implementing it in three case-studies and assessing its ability to address the consistency management requirements. These case-studies demonstrate the framework’s ability to address these consistency management requirements. We also identified several limitations of the framework and discuss possible ways to address them. The dynamic, data-driven, and interconnected nature of DTs introduces various consistency challenges beyond model-level ones. A key example is behavioral inconsistency between the RE and its VE, detecting which is essential for ensuring equal outcomes, i.e., a mimicry relation. We present a case-study implementing a DT using machine learning (ML) and 3D models to detect inconsistencies between the expected behavior (encapsulated with a VE) and the actual behavior of an autonomous soccer robot (RE). This demonstrates the feasibility of using DTs for automated inconsistency detection. Finally, despite being a well-known research topic, consistency management has not been sufficiently explored in the context of DTs. Our research on DT model consistency management and RE-VE inconsistency detection addresses this research gap. We demonstrate the relevance and applicability of our methods with various casestudies. Furthermore, we strongly believe that continued research is essential in identifying further possible consistency issues in the context of DTs and in developing methods for identifying and addressing them. The concept of DTs have enabled a wide range of use-cases and will continue to drive new ones. To fully realize their potential, DT engineering must be supported by appropriate tools and methods, with consistency management as a foundational concern throughout the lifecycle.
Link: Direct from TU Eindhoven
Engineering Doctorate (EngD) thesis
Title:
Model as a Service : Towards a Discovery Platform for Internet of Food
Abstract:
The Internet of Food (INoF) consortium, which is part of Sustainable Food Initiative (SFI), aims to address the future food safety challenges using engineering solutions to make the production process more efficient and sustainable. Inter-organization collaboration can stimulate fast innovation and sustainable research processes by significantly reducing data loss as well as miscommunication. Such collaboration requires an appropriate digital infrastructure that can maintain interoperability among diverse data formats from different sources. This infrastructure should also be able to facilitate sharing of data and services without companies having to share IP (Intellectual Property) or replicate corresponding execution environments. As part of the INoF, this project aims to develop a prototype for such infrastructure and set up a baseline for building an effective model discovery platform. In this context, models are computational units that can provide insights into food products. Having access to results from more models, companies can make better decisions and speed up product development. During this project, a microservice based architecture was de-signed and a prototype was developed that exploited the idea of Model as a Service (MaaS). It has the functionality to offer models in the form of web services allowing organizations other than the owner of the models to use them. For achieving interoperability among different data sources in the context of this project, functionalities, such as dynamic model parameter mapping and on-demand unit conversion, were implemented into this prototype. After execution, results from several models belonging to different organizations can also be viewed through this platform. One of the major goals of this project was to demonstrate the benefits and possibilities of sharing model results to attract further collaboration. Therefore, several INoF partners were closely involved in this project. The MaaS prototype was also demonstrated to all the INoF partners and earned quite a few appreciations.
Location: Unilever Netherlands and Eindhoven University of Technology
Link: Direct from TU Eindhoven or Cached in Github
Master thesis
Title:
Similarity Analysis Framework for Software Product Line Extraction
Abstract:
In a typical software vendor setting, creating a Software Product Line (SPL) from shared components across projects can streamline development and enhance stability, though maintaining it requires significant effort to prevent obsolescence. FEV GmbH has conducted research to identify extrinsic, semantic, and structural similarities among software components, employing the PERSIST guideline for extrinsic similarity calculation. The structural analysis method matches interface signals to gauge similarity and extract a generic core for future use, while the semantical analysis transforms test specifications into Input/Output Extended Finite Automata to establish simulation relations. The overarching aim of this research is to develop a Similarity Analysis framework (SimA) integrating these methods to efficiently identify component similarities, generate informative reports, and maintain the SPL. In this context, current outdated extrinsic analysis tool is updated to match current projects structure, which is later integrated with existing structural and semantical analysis tools, forming the foundation of the Similarity Analysis Framework for generating and evaluating similarity reports.
Location: FEV GmbH, Germany and Software Engineering chair (I3) of RWTH Aachen University
Link: Cached in Github