Design, Test, and Maintain With Digital Twins

3Q 2017 | Technology Analysis Report | AN-2649 | 19 pages | 1 table | 3 charts | 3 figures | PDF

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Table of Contents

  • 1. INTRODUCTION TO DIGITAL TWINS
    • 1.1. Report Definitions
    • 1.2. Levels of Digital Twins
    • 1.3. Advantages of Digital Twins
    • 1.4. Integrating Digital Twins into Current Processes
    • 1.5. Drivers of Digital Twin Adoption
    • 1.6. Inhibitors
    • 1.7. Recent Activity in Digital Twins
  • 2. FORECASTS
    • 2.1. Methodology
    • 2.2. Condition-Based Monitoring Connections
    • 2.3. Manufacturing Value Added Due to Digital Twins
  • 3. VENDOR LANDSCAPE
  • 4. RECOMMENDATIONS


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A digital twin is a virtual model of a process, product or service that is used to detect issues, test and simulate scenarios on the physical model. Currently, companies such as GE use digital twins for airplane engines and steam turbines. NASA has used pairing technology for decades with spacecraft. TWI monitors and maintains offshore wind turbines using digital twins, and Bureau Veritas uses them to monitor other offshore platforms and assets. National Instruments has a platform for embedded connections to create digital twins for HVAC systems and grid-tied energy inverters. All of these solutions empower end users to predict and plan for faults and optimize performance of their asset. 
 
This report examines the current state of digital twins and describes the different levels and capabilities of digital twins. The “Forecasts” section displays ABI Research’s condition-based monitoring and digital twins manufacturing value add forecasts for nine countries. The “Vendor landscape” section focuses on a wide variety of relevant vendors that contribute to digital twins and the digital twin ecosystem or have adopted or committed to the technology in various ways.

Table of Contents

  • 1. INTRODUCTION TO DIGITAL TWINS
    • 1.1. Report Definitions
    • 1.2. Levels of Digital Twins
    • 1.3. Advantages of Digital Twins
    • 1.4. Integrating Digital Twins into Current Processes
    • 1.5. Drivers of Digital Twin Adoption
    • 1.6. Inhibitors
    • 1.7. Recent Activity in Digital Twins
  • 2. FORECASTS
    • 2.1. Methodology
    • 2.2. Condition-Based Monitoring Connections
    • 2.3. Manufacturing Value Added Due to Digital Twins
  • 3. VENDOR LANDSCAPE
  • 4. RECOMMENDATIONS