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E-raamat: Digital Twin Development and Deployment on the Cloud: Developing Cloud-Friendly Dynamic Models Using Simulink(R)/SimscapeTM and Amazon AWS

(Assistant Professor, Mechanical Engineering, Prince Mohammad Bin Fahd University, KSA.), (Senior Controls Engineer, Cummins Emissions Solutions, Columbus, IN, USA), (Technical Advisor with KPIT Infosystems Inc, Columbus, IN, USA)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 24-May-2020
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128216460
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Digital Twin Development and Deployment on the Cloud: Developing Cloud-Friendly Dynamic Models Using Simulink(R)/SimscapeTM and Amazon AWSSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 24-May-2020
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128216460
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Digital Twin Development and Deployment in the Cloud: Developing Cloud-Friendly Dynamic Models Using Simulink®/SimscapeTM and Amazon AWS promotes a physics-based approach to the field of digital twins. Through the use of multiphysics models running in the cloud, significant improvement to the diagnostics and prognostic of systems can be attained. The book draws a clear definition of digital twins, helping business leaders clearly identify the value it brings. In addition, it outlines the key elements needed for deployment, including the hardware and software tools needed. Special attention is paid to the process of developing and deploying the multi-physics models of the digital twins.

  • Provides a high-level overview of digital twins and their underutilization in the field of asset management and maintenance
  • Proposes a streamline process to create digital twins for a wide variety of applications using MATLAB® Simscape™
  • Deploys developed digital twins on Amazon Web Services
  • Includes MATLAB and Simulink codes available for free download on MATLAB central
  • Covers popular prototyping hardwares, such as Arduino and Raspberry Pi
Acknowledgments xi
1 Added value of digital twins and IoT
1(10)
1.1 Introduction
1(1)
1.2 Motivation to write this book
2(1)
1.3 Digital twins
3(1)
1.4 On-board and off-board diagnostics
3(2)
1.5 Modeling and simulation software
5(3)
1.6 Organization and outline of the book
8(2)
References
10(1)
2 Cloud and IoT technologies
11(10)
2.1 Overview
11(1)
2.2 History of cloud
11(1)
2.3 Evolution of cloud technologies
12(1)
2.4 Connecting machines to the cloud
13(1)
2.5 Applications
13(5)
2.6 Considerations for cloud services
18(1)
2.7 What is edge computing?
19(1)
2.8 Edge computing versus cloud computing
19(1)
2.9 Edge and cloud computing examples
20(1)
2.10 Will 5G accelerate cloud computing?
20(1)
3 Digital twin model creation of a robotic arm
21(50)
3.1 Introduction
21(2)
3.2 Hardware parameters
23(1)
3.3 Simulation process
24(41)
3.4 Application problem
65(6)
4 Ball on plate modeling
71(40)
4.1 Introduction
71(1)
4.2 Ball on plate hardware
71(1)
4.3 Block diagram of the ball on plate system
72(1)
4.4 Failure modes and diagnostics concept for the ball on plate
73(1)
4.5 Simscape model for the ball on plate
74(21)
4.6 Ball Plate Interaction
95(6)
4.7 S-function for the Ball Plate Interaction
101(5)
4.8 Simulation of the model
106(5)
5 Digital twin model creation of double mass spring damper system
111(26)
5.1 Introduction
111(1)
5.2 Hardware parameters
112(1)
5.3 Simulation process
112(23)
5.4 Application problem
135(2)
6 Digital twin model creation of solar panels
137(26)
6.1 Introduction
137(1)
6.2 Photovoltaic hardware setup
137(3)
6.3 Experimental data collection for model creation
140(1)
6.4 PV system simscape model
141(1)
6.5 Solar cell modeling of the PV system
141(4)
6.6 Solar cell modeling of the PV subsystem
145(5)
6.7 Simulation results
150(6)
6.8 Application problem
156(6)
References
162(1)
7 Digital twin development for an inverter circuit for motor drive systems
163(40)
7.1 Introduction
163(2)
7.2 Block diagram of the motor drive inverter system
165(1)
7.3 Failure modes and diagnostics concept of the motor drive inverter system
165(1)
7.4 Simscape model of the motor drive inverter system
165(7)
7.5 Fault injection and diagnostic algorithm development
172(2)
7.6 Application problem
174(27)
Reference
201(2)
8 Digital Twin development and cloud deployment for a Hybrid Electric Vehicle
203(136)
8.1 Introduction
203(1)
8.2 Hybrid Electric Vehicle physical asset/hardware setup
204(5)
8.3 Block diagram of the Hybrid Electric Vehicle system
209(1)
8.4 Failure modes and diagnostic concept of Hybrid Electric Vehicle system
209(2)
8.5 Simscape™ model of a Hybrid Electric Vehicle system
211(16)
8.6 EDGE device setup and cloud connectivity
227(33)
8.7 Digital Twin Modeling and calibration
260(1)
8.8 Off-board diagnostics algorithm development for Hybrid Electric Vehicle system
260(23)
8.9 Deploying digital twin Hybrid Electric Vehicle system model to cloud
283(51)
8.10 Application problem
334(4)
References
338(1)
9 Digital Twin Development and cloud deployment for a DC Motor Control embedded system
339(174)
9.1 Introduction
339(2)
9.2 Setting up Real-Time Embedded Controller Hardware and Software for DC Motor Speed Control
341(8)
9.3 Open-Loop Data Collection and Closed-Loop PID Controller Development for the DC Motor Hardware
349(19)
9.4 Developing Simscape™ Digital Twin model for the DC motor
368(14)
9.5 Parameter tuning of the Simscape™ DC Motor Model with data from DC motor hardware using Simulink® parameter estimation TM tool
382(15)
9.6 Adding AWS cloud connectivity to real-time embedded hardware for DC Motor Speed Control
397(61)
9.7 Off-Board Diagnostics/prognostics algorithm development for DC Motor Controller Hardware
458(22)
9.8 Deploying the Simscape™ Digital Twin Model to the AWS cloud
480(22)
9.9 Application problem
502(10)
References
512(1)
10 Digital twin development and deployment for a wind turbine
513(60)
10.1 Introduction
513(2)
10.2 Physical asset setup and considerations: wind turbine hardware
515(1)
10.3 Understanding the input--output behavior of the wind turbine Simscape™ model
515(11)
10.4 Developing the driver Simscape™ model for the hardware and communicating to AWS
526(19)
10.5 Deploying the Simscape™ digital twin model to the AWS cloud and performing Off-BD
545(24)
10.6 Application problem
569(3)
Reference
572(1)
Index 573
Dr. Khaled has extensive industrial and academic experience in the field of dynamics, controls and IoT solutions. He is currently an Assistant professor in Prince Mohammad Bin Fahd University. He is an innovator with more than 30 patents and patent applications in the fields of smart systems and energy. He is the author of "Practical Design and Application of Model Predictive Control". He also has numerous publications in the field of controls and autonomous navigation. Dr. Khaled is a green-belt six sigma certified. He received the status of "Outstanding Researcher" granted by the U.S Government in 2012. Bibin has a Master of Science in Mechanical engineering and 12 years of industrial experience in the field of Controls Design, Software Development and Rapid Prototyping. He is currently working as a Technical Advisor with KPIT Technologies Inc, USA. Bibin has worked on vehicle, aftertreatment, air-handling and engine modelling and controls and on board diagnostic development. He is an expert in Matlab and Simulink as well as Hardware and Software solutions for the control of vehicle and powertrain systems. He has 7 patents and several patent applications and published 5 journal and conference papers. Bibin is the co-author of "Practical Design and Application of Model Predictive Control". Affan Siddiqui is currently working in Cummins Emissions Solutions as a Senior Controls Engineer. He has 4 years of experience in software development of control algorithms and diagnostics of diesel engine aftertreatment systems. Affan specializes in embedded control systems using Matlab Simulink and is an expert in the urea doser control portion of the aftertreatment system with 1 patent application. Before his work in Cummins, Affan acquired a Master of Science degree in Mechanical Engineering from Virginia Tech. His masters thesis "A New Inspection Method Based on RGB-D Profiling" is based on an inexpensive autonomous railway and road mapping system using Robot Operating System (ROS) and Microsoft Kinect cameras.
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