[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127592-en":3,"doc-seo-127592-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127592,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Cognitive Digital Twin using Knowledge Graph and Machine Learning - MSc Individual Research Project","Creating digital twins requires integrating the right technology infrastructure to achieve system autonomy, while handling complexities in interactions, limited agility, organizational culture, and data availability and relationships. Cognitive twins represent the next generation by adding cognitive capabilities through knowledge graphs and machine learning models. The knowledge graph encodes maintenance-domain entities and interrelationships and supports strategic decision-making options. This project proposes a structured framework, models cognitive digital twins via knowledge graph approaches, and validates it using a UAV fuel-system case study, showing decision support for maintenance questions.","CRANFIELD UNIVERSITY  \nSuresh West Landon-Valdez  \nCognitive digital twin using knowledge graph and machine learning  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING  \nMSc Individual Research Project  \nMSc  \nAcademic Year: 2021-2022  \nSupervisor: Dr Samir Khan Associate Supervisor: Dr Cristina Latsou  \nCRANFIELD UNIVERSITY  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING  \nIndividual Research Project  \nMSc  \nAcademic Year 2021-2022  \nSuresh Wes Landon-Valdez  \nCognitive digital twin using knowledge graph and machine learning  \nSupervisor: Dr Samir Khan Associate Supervisor: Dr Christina Latsou August 2022  \nThis thesis is submitted in partial fulfilment of the requirements for the degree of MSc in management and information systems  \n(NB. This section can be removed if the award of the degree is based solely on examination of the thesis)  \n© Cranfield University 2022. All rights reserved. No part of this publication may be reproduced without the written permission of the  \ncopyright owner.  \nABSTRACT  \nWith many opportunities emerging in creating digital twins, the key to creating system autonomy requires integrating technology infrastructure. This is a challenging task , and its implementation requires right mix of technologies, domain expertise, and partnership ecosystems. Investing efforts help overcome the various complexities during system interactions , the lack of agility, organisational cultures, comprehensive data sets and the relationship between data.  \nCognitive Twins are being championed as the next generation Digital Twins which are equipped with cognitive capabilities. This is enabled by using knowledge graphs and machine learning models to offer insights and decision-making options to multiple domain. In particular, the knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a maintenance process. It also contains information on possible decision-making options that can assist decision-makers at the strategic level.  \nIn this project, a structured framework will be proposed to develop a novel Cognitive Digital Twin framework to integrate the relevant enabling technologies. For this , a knowledge graph modelling approach will be used to construct cognitive digital twins that capture specific knowledge related to the maintenance domain.  \nThis project will demonstrate the benefits of integrating concepts of knowledge graph modelling to develop cognitive digital twins. The key requirement to be satisfied here is demonstrating a cognitive digital twin that can make decisions. The developed framework is successfully validated by its application to a real case study taken from previous paper on UAV fuel system. The results show that CDT is able to use cognitive capabilities to answer questions related to maintenance and deliver decision-making options to the users.  \nKeywords:  \nDigital Twin, Cognitive Digital Twin, Cognitive Twin, Knowledge Graph, Semantic modelling  \nACKNOWLEDGEMENTS  \nFirst and foremost, I want to express my gratitude to my supervisors Dr Christina Latsou and Dr Samir Khan for guiding me for the last four months on my thesis. Both supervisor’s advice and assistance were essential in completing the thesis successfully.  \nI would also like to thank the project sponsors, Mr Andy Box and the whole team from Leonardo for giving feedback, the guidance and sharing their experience for the development of the project.  \nBesides, I would like to thank all my friends in Cranfield University for their company in long hours at the library and the coffee breaks. Last but not least , special thanks to my lovely parents and my girlfriend for giving me support and motivation from all the way in Barcelona.  \nTABLE OF CONTENTS  \nABSTRACT ...................................................................................................................... i  \nACKNOWLEDGEMENTS.......................................................................................","cbCainWJKOQesiKS","https://ap.wps.com/l/cbCainWJKOQesiKS","pdf",2824124,2,1,63,"English","en",105,"# 1 Introduction\n## 1.1 Research Background\n## 1.1 Out of Scope\n## 1.2 Aim\n## 1.3 Objectives\n## 1.4 Structure of the Thesis\n# 2 Literature Review\n## 2.1 Digital Twin\n## 2.2 Cognitive Digital Twin\n## 2.3 Knowledge Graph\n## 2.4 Knowledge Graph in Cognitive Digital twin\n## 2.5 Researc","[{\"question\":\"What technologies are proposed to enable cognitive capabilities in digital twins?\",\"answer\":\"The project uses knowledge graphs and machine learning models to provide cognitive capabilities, insights, and decision-making options across multiple domains.\"},{\"question\":\"How does the knowledge graph contribute to maintenance-related decision-making?\",\"answer\":\"It models domain-specific entities and relationships within the maintenance process and includes possible decision-making options that can assist strategic decision-makers.\"},{\"question\":\"How was the proposed cognitive digital twin framework validated?\",\"answer\":\"It was validated through a real case study drawn from prior work on a UAV fuel system, demonstrating that the developed framework can make decisions and answer maintenance-related questions.\"}]","Cognitive Digital Twin using Knowledge Graph and Machine Learning - 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