Digital twin (DT) technology allows the user to monitor the asset, specifically over the operation and service phase of the life cycle, which is the longest-lasting phase for complex engineering assets. This paper aims to present a thematic review of DTs in terms of the technology used, applications, and limitations specifically in the context of maintenance. This review includes a systematic literature review of 59 articles on semantic digital twins in the maintenance context. Key performance indicators and explanations of the main concepts constituting the DT have been presented. This article contains a description of the evolution of DTs together with their characterisation for maintenance purposes. It provides an ontological approach to develop DT and improve the maintenance management leading to the creation of a structured DT or a Cognitive Twin (CT). Moreover, it points out that using a top-level ontology approach should be the starting point for the creation of CT. Enabling the creation of the digital framework that will break down silos, ensuring a perfect integration in a network of twins’ scenario. 

Cognitive digital twin: An approach to improve the maintenance management

An Ontology-based Engineering system to support aircraft manufacturing system design

Engineered solutions are becoming more complex and multi‐disciplinary in nature. This evolution requires new techniques to enhance design and analysis tasks that incorporate data integration and interoperability across various engineering tool suites spanning multiple domains at different abstraction levels. Semantic web technologies (SWT) offer data integration and interoperability benefits as well as other opportunities to enhance reasoning across knowledge represented in multiple disparate models. This paper introduces the digital engineering framework for integration and interoperability (DEFII) for incorporating SWT into engineering design and analysis tasks. The framework includes three notional interfaces for interacting with ontology‐aligned data. It also introduces a novel model interface specification diagram (MISD) that provides a tool‐agnostic model representation enabled by SWT that exposes data stored for use by external users through standards‐based interfaces. Use of the framework results in a tool‐agnostic authoritative source of truth spanning the entire project, system, or mission.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sys.21662

Driving digital engineering integration and interoperability through semantic integration of models with ontologies

Abstract Digital Twin technology has been widely applied in various industry domains. Modern industrial systems are highly complex consisting of multiple interrelated systems, subsystems and components. During the lifecycle of an industrial system, multiple digital twin models might be created related to different domains and lifecycle phases. The integration of these relevant models is crucial for creating higher-level intelligent systems. The Cognitive Digital Twin (CDT) concept has been proposed to address this challenge by empowering digital twins with augmented semantic capabilities. It aims at identifying the dynamics and interrelationships of virtual models, thus to enhance complexity management capability and to support decision-making during the entire system lifecycle. This paper aims to explore the CDT concept and its core elements following a systems engineering approach. A conceptual architecture is designed according to the ISO 42010 standard to support CDT development; and an application framework enabled by knowledge graph is provided to guide the CDT applications. In addition, an enabling tool-chain is proposed corresponding to the framework to facilitate the implementation of CDT. Finally, a case study is conducted, based on simulation experiments as a proof-of-concept. 

/pdf/exploring-the-concept-of-cognitive-digital-twin-from-model-1ub0v7r8.pdf

Exploring the concept of Cognitive Digital Twin from model-based systems engineering perspective

Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies.

Sustainable Digital Marketing Under Big Data: An AI Random Forest Model Approach

/pdf/design-ontology-supporting-model-based-systems-engineering-2usrudr7g4.pdf

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Digital twin (DT) provides a solution for supporting the interconnection between the physical world and the virtual world. When implementing DT integration, it is challenging to implement interface definition, information and service integration across DTs. This paper proposes a semantic modeling approach with a High-Level Architecture (HLA) to support the DT integration. The semantic modeling approach based on Graph-Object-Property-Point-Role-Relationship (GOPPRR) meta-meta models is used to realize the integrated formalisms of heterogeneous DTs. HLA is used to support interface definition and service integration between virtual entities of DT. Finally, a case of an unmanned aerial vehicle (UAV) landing on ship is used to verify the flexibility of this approach. From the results, we find the GOPPRR ontology and HLA specification enables to provide a unified formalism of the DTs of UAV and the ship, and to implement data exchange during the distributed simulation execution.

Supporting Digital Twin Integration Using Semantic Modeling and High-Level Architecture.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Han Li

Author Tools

Chat about Author

Papers

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Supporting Digital Twin Integration Using Semantic Modeling and High-Level Architecture.