Digital Twin

Introduction

Soumendra kumar sahoo
·May 16, 2021·

2 min read

Digital Twin

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Overview

A Digital Twin is a virtual copy of a physical device. It simulates the behaviour of the physical device based on the constant stream of data received through its sensors.

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

Another detailed definition

A digital twin is a dynamic virtual representation of a physical object or system, usually across multiple stages of its lifecycle. It uses real-world data, simulation or machine learning models, combined with data analysis, to enable understanding, learning, and reasoning. Digital twins can be used to answer what-if questions and should be able to present the insights in an intuitive way.

source: IBM

For e.g., Google Maps is a Digital Twin of the earth for a specific industry context.

Existing Problems

These are few problems which Digital Twin wants to solve:

  • Making physical devices transparent
  • Real-time data monitoring of a digital component
  • Proactive anomaly/failure detection. For e.g. predicting MTTF and MTBF* of a Part/Product.
  • Predictive suggestions on optimizing the efficiency of a physical device and overall plant production yield.

Categories of Digital Twins

There are three categories of Twins:

  1. Part Twin/Discrete Twin
  2. Product Twin/Composite Twin
  3. System Twin/Digital Twin for Organization

Example: A wind energy alternator system

image.png Part Twin is one intrinsic part of an alternator/turbine that rotates to produce electricity

image.png Product Twin is the aggregation of multiple Part twins which twins an entire turbine.

image.png System Twin twins the entire Wind turbine system on an organizational level.

Each of these categories is further divided into the following three kinds:

  1. Status: Provides the status of the device. Generally created with visualization tools.
  2. Operational: Provides more info than the Status twins. These twins can change the parameters and implement actions.
  3. Simulation: Combining historical data with real-time live feed predicts future states, detects anomalies, optimization opportunities can improve the recovery yield of plants.

image.png

References and Further reading

Appendix

MTTF

  • The average time hardware runs before falling permanently.
  • This metrics is used where there is a very low to no chance of repairability of products.

MTBF

  • The average time required to repair hardware and making it up back into production.

image.png Sample Diagram to understand MTTF and MTBF

Photo by Joshua Sortino on Unsplash

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