Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

pdf/digital-twin-enabling-technologies-challenges-and-open-2aylj47ira.pdf

Digital Twin: Enabling Technologies, Challenges and Open Research

Digital twin modeling

Conceptual digital twin modeling based on an integrated five-dimensional framework and TRIZ function model

Untimely machinery breakdown will incur significant losses, especially to the manufacturing company as it affects the production rates. During operation, machines generate vibrations and there are unwanted vibrations that will disrupt the machine system, which results in faults such as imbalance, wear, and misalignment. Thus, vibration analysis has become an effective method to monitor the health and performance of the machine. The vibration signatures of the machines contain important information regarding the machine condition such as the source of failure and its severity. Operators are also provided with an early warning for scheduled maintenance. Numerous approaches for analyzing the vibration data of machinery have been proposed over the years, and each approach has its characteristics, advantages, and disadvantages. This manuscript presents a systematic review of up-to-date vibration analysis for machine monitoring and diagnosis. It involves data acquisition (instrument applied such as analyzer and sensors), feature extraction, and fault recognition techniques using artificial intelligence (AI). Several research questions (RQs) are aimed to be answered in this manuscript. A combination of time domain statistical features and deep learning approaches is expected to be widely applied in the future, where fault features can be automatically extracted from the raw vibration signals. The presence of various sensors and communication devices in the emerging smart machines will present a new and huge challenge in vibration monitoring and diagnosing.

/pdf/vibration-analysis-for-machine-monitoring-and-diagnosis-a-2cegy8dxlj.pdf

Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review

Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis

The ever-increasing functional density and complexity of the satellite systems, the harsh space flight environment, as well as the cost reduction measures that require less operator involvement are increasingly driving the need to develop new approaches for fault diagnosis and health monitoring (FD-HM). The data-driven FD-HM approaches use signal processing or data mining to obtain implicit information for the operating state of the system, which is good at monitoring systems extensively and shallowly and is expected to reduce the workload of the operators. However, these approaches for the FD-HM of the satellite system are driven primarily by the historical data and some static physical data, with little consideration for the simulation data, real-time data, and data fusion between the two, so it is not fully competent for the real-time monitoring and maintenance of the satellite in orbit. To ensure the reliable operation of the complex satellite systems, this paper presents a new physical–virtual convergence approach, digital twin, for FD-HM. Moreover, we present an FD-HM application of the satellite power system to demonstrate the effectiveness of the proposed approach.

A Digital Twin-Based Approach for the Fault Diagnosis and Health Monitoring of a Complex Satellite System

Cyber-physical system (CPS) is a new trend in the complex system related research works, where network connectivity enhances computing power and systemic behavior emerges through the competition, interaction, collaboration and integration among individual interweaving, which consists of real-time monitoring, data management, physical feedback control. From this perspective, CPS is a dynamic entity with rich functions. However, designers may encounter a difficult situation, in which subsequent dynamic changes of the system are discussed and appropriate functionalities are added in the early design phase. Since the digital twin is the digital duplicate of the physical entity, it can dynamically evolve following the product life cycle. In this paper, we propose a hierarchical digital twin model framework for CPS design. In the light of digital twin concept, the hierarchical high-level models facilitate storage of information from the entire product life cycle. Finally, an industrial robot application is presented to demonstrate the efficacy of the model framework.

A Hierarchical Digital Twin Model Framework for Dynamic Cyber-Physical System Design

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.

Liping Chen

Author Tools

Chat about Author

Papers

A Digital Twin-Based Approach for the Fault Diagnosis and Health Monitoring of a Complex Satellite System

A Hierarchical Digital Twin Model Framework for Dynamic Cyber-Physical System Design