Digital Twin Technology—A Review and Its Application Model for Prognostics and Health Management of Microelectronics
Abstract
:1. Introduction
2. Related Work—Development and Definition
2.1. Digitalisation Stages and Highlights
2.2. The Definition
“an integrated multi-physics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin”.
Digital Twin is a continuously updated multi-physics, multiscale, probabilistic simulation model of a physical entity (an object, a system, or a process) utilising big data, bilateral connectivity, and advanced software analytics to provide product monitoring, diagnostics, prognostics, and optimisation services.
3. Related Work—Adoption in Industry
3.1. Industry 4.0 and Smart Manufacturing
3.2. Health, Telecommunication, and Other Industries
3.3. Reliability of Electronic Systems
4. Methodology—Digital Twin Architecture
4.1. Basic Structure
4.2. Types of Connections
4.3. Implementation Scales
5. Evaluation—Digital Twin Models
5.1. Three- and Five-Dimensional Models
5.2. Generalised Two-Branched Model
5.3. Product-Specific Monitoring Device
6. Implementation—PHM Workflow
Building Blocks of Hybrid PHM
7. Conclusions and Outlook
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Stage | Description | Year | Highlights |
---|---|---|---|
1. | Digital Enablement | 1950s |
|
1970s |
| ||
2. | Digitalisation Assistance | 1980s |
|
3. | Digital Control and Link | 1990s |
|
4. | Cyber-Physical Integration | 2000s |
|
2010s |
| ||
2020s |
|
Source | Definition |
---|---|
[13] | an integrated multiphysics, multiscale, probabilistic simulation of a complex product, which functions to mirror the lifecycle of its corresponding physical twin. |
[16] | a comprehensive physical and functional description of a component, product or system, which includes more or less all information which could be useful in all the (current and subsequent) lifecycle phases. |
[18] | a collection of model-based simulations and data analytics, necessitated by requirements of the modern competitive industrial environment at all stages of design and production, to predict the outcome, optimize, correct and evaluate. |
[20] | an integrated multiphysics, multiscale, probabilistic, and ultra-realistic simulation of systems or products which can mirror the life of its corresponding twin using available physical models, history data, and real-time data. |
[23] | a multiphysics and multiscale simulation model that mirrors the corresponding physical twin, allowing the extension of the simulation to all life cycle phases of the system. |
[24] | an organic whole of a physical asset or entity as well as its digitized representation, which mutually communicate, promote, and co-evolve through bidirectional interactions. |
[26] | an operational replica that can be used for testing, commissioning, and training. |
[27] | a replication of real physical production system, that enables bidirectional control with the physical process and is used for system optimization, monitoring, diagnostics and prognostics using the integration of artificial intelligence, machine learning and software analytics with a large volume of data from physical systems. |
[28] | a virtual equivalent of an actual physical product or service. |
[31] | a comprehensive digital representation of an individual product that includes the properties, condition, and behaviour of the real-life object through a set of realistic models and data, which can simulate its actual behaviour in the deployed environment. |
[32] | a technology enabling the replication of the development and manufacturing of a product or production system over the course of its entire lifecycle, and to thereby predict behaviour, optimize operational utilization and apply knowledge gained in the context of earlier design and production efforts. |
[33] | a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. |
[34] | an instantiated model (numerical, analytical, hybrid) of a specific asset or device, which is deployed (in the cloud or on an edge device) and connected to the physical device, where the connection may be established through sensors installed at the device or other sources collecting specific information, delivering a continuous data stream fed into the model or as boundary condition or as reference value. |
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Inamdar, A.; van Driel, W.D.; Zhang, G. Digital Twin Technology—A Review and Its Application Model for Prognostics and Health Management of Microelectronics. Electronics 2024, 13, 3255. https://doi.org/10.3390/electronics13163255
Inamdar A, van Driel WD, Zhang G. Digital Twin Technology—A Review and Its Application Model for Prognostics and Health Management of Microelectronics. Electronics. 2024; 13(16):3255. https://doi.org/10.3390/electronics13163255
Chicago/Turabian StyleInamdar, Adwait, Willem Dirk van Driel, and Guoqi Zhang. 2024. "Digital Twin Technology—A Review and Its Application Model for Prognostics and Health Management of Microelectronics" Electronics 13, no. 16: 3255. https://doi.org/10.3390/electronics13163255