scholarly journals Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho–Kalman Method for Order Estimation

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 431
Author(s):  
Alexios Papacharalampopoulos
Keyword(s):  
System Identification ◽  
Time Domain ◽  
Data Driven ◽  
Driven Systems ◽  
Control Techniques ◽  
Digital Twin ◽  
Order Estimation ◽  
Digital Twins ◽  
Initial Stage ◽  
System Order

System identification has been a major advancement in the evolution of engineering. As it is by default the first step towards a significant set of adaptive control techniques, it is imperative for engineers to apply it in order to practice control. Given that system identification could be useful in creating a digital twin, this work focuses on the initial stage of the procedure by discussing simplistic system order identification. Through specific numerical examples, this study constitutes an investigation on the most “natural” method for estimating the order from responses in a convenient and seamless way in time-domain. The method itself, originally proposed by Ho and Kalman and utilizing linear algebra, is an intuitive tool retrieving information out of the data themselves. Finally, with the help of the limitations of the methods, the potential future outlook is discussed, under the prism of forming a digital twin.

scholarly journals A Five-Step Approach to Planning Data-Driven Digital Twins for Discrete Manufacturing Systems

2021 ◽  
Vol 11 (8) ◽  
pp. 3639
Author(s):  
Matevz Resman ◽  
Jernej Protner ◽  
Marko Simic ◽  
Niko Herakovic
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Multiple Scales ◽  
Building Blocks ◽  
Heterogeneous Data ◽  
Digital Model ◽  
Data Driven ◽  
Digital Models ◽  
Digital Twin ◽  
Digital Twins

A digital twin of a manufacturing system is a digital copy of the physical manufacturing system that consists of various digital models at multiple scales and levels. Digital twins that communicate with their physical counterparts throughout their lifecycle are the basis for data-driven factories. The problem with developing digital models that form the digital twin is that they operate with large amounts of heterogeneous data. Since the models represent simplifications of the physical world, managing the heterogeneous data and linking the data with the digital twin represent a challenge. The paper proposes a five-step approach to planning data-driven digital twins of manufacturing systems and their processes. The approach guides the user from breaking down the system and the underlying building blocks of the processes into four groups. The development of a digital model includes predefined necessary parameters that allow a digital model connecting with a real manufacturing system. The connection enables the control of the real manufacturing system and allows the creation of the digital twin. Presentation and visualization of a system functioning based on the digital twin for different participants is presented in the last step. The suitability of the approach for the industrial environment is illustrated using the case study of planning the digital twin for material logistics of the manufacturing system.

scholarly journals Beyond building predictive models: TwinOps in biomanufacturing

2021 ◽  
Author(s):  
Elisa Canzani ◽  
Sander W. Timmer
Keyword(s):  
Best Practices ◽  
Predictive Models ◽  
Paradigm Shift ◽  
Digital Technologies ◽  
Data Driven ◽  
Biopharmaceutical Industry ◽  
Digital Twin ◽  
Digital Twins ◽  
Technical Challenges ◽  
And Control

<div>On the wave of more and more manufacturers embracing the pervasive mission to build digital twins, also biopharmaceutical industry envisions a significant paradigm shift of digitalisation towards an intelligent factory where bioprocesses continuously learn from data to optimise and control productivity. While extensive efforts are made to build and combine the best mechanistic and data-driven models, there has not been a complete digital twin application in pharma. One of the main reasons is that production deployment becomes more complex regarding the possible impact such digital technologies could have on vaccine products and ultimately on patients. To address current technical challenges and fill regulatory gaps, this paper explores some best practices for TwinOps in biomanufacturing – from experiment to GxP validation – and discusses approaches to oversight and compliance that could work with these best practices towards building bioprocess digital twins at scale.</div>

scholarly journals Beyond building predictive models: TwinOps in biomanufacturing

2021 ◽  
Author(s):  
Elisa Canzani ◽  
Sander W. Timmer
Keyword(s):  
Best Practices ◽  
Predictive Models ◽  
Paradigm Shift ◽  
Digital Technologies ◽  
Data Driven ◽  
Biopharmaceutical Industry ◽  
Digital Twin ◽  
Digital Twins ◽  
Technical Challenges ◽  
And Control

<div>On the wave of more and more manufacturers embracing the pervasive mission to build digital twins, also biopharmaceutical industry envisions a significant paradigm shift of digitalisation towards an intelligent factory where bioprocesses continuously learn from data to optimise and control productivity. While extensive efforts are made to build and combine the best mechanistic and data-driven models, there has not been a complete digital twin application in pharma. One of the main reasons is that production deployment becomes more complex regarding the possible impact such digital technologies could have on vaccine products and ultimately on patients. To address current technical challenges and fill regulatory gaps, this paper explores some best practices for TwinOps in biomanufacturing – from experiment to GxP validation – and discusses approaches to oversight and compliance that could work with these best practices towards building bioprocess digital twins at scale.</div>

Detecting malicious behavior in social platforms via hybrid knowledge- and data-driven systems

2021 ◽  
Author(s):  
Jose N. Paredes ◽  
Gerardo I. Simari ◽  
Maria Vanina Martinez ◽  
Marcelo A. Falappa
Keyword(s):  
Data Driven ◽  
Driven Systems ◽  
Malicious Behavior ◽  
Hybrid Knowledge

Digital Twin for Smart Manufacturing: The Practitioner’s Perspective

2020 ◽  
Author(s):  
Maja Bärring ◽  
Björn Johansson ◽  
Goudong Shao
Keyword(s):  
Conceptual Development ◽  
Production Systems ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Adoption And Implementation ◽  
New Information ◽  
Digital Twins ◽  
Discrete Manufacturing ◽  
Design Production

Abstract The manufacturing sector is experiencing a technological paradigm shift, where new information technology (IT) concepts can help digitize product design, production systems, and manufacturing processes. One of such concepts is Digital Twin and researchers have made some advancement on both its conceptual development and technological implementations. However, in practice, there are many different definitions of the digital-twin concept. These different definitions have created a lot of confusion for practitioners, especially small- and medium-sized enterprises (SMEs). Therefore, the adoption and implementation of the digital-twin concept in manufacturing have been difficult and slow. In this paper, we report our findings from a survey of companies (both large and small) regarding their understanding and acceptance of the digital-twin concept. Five supply-chain companies from discrete manufacturing and one trade organization representing suppliers in the automotive business were interviewed. Their operations have been studied to understand their current digital maturity levels and articulate their needs for digital solutions to stay competitive. This paper presents the results of the research including the viewpoints of these companies in terms of opportunities and challenges for implementing digital twins.

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