Why is a Digital Twin Crucial for IoT?

Applied Tech Review | Thursday, April 28, 2022

Digital twins are virtual representations of real devices that data scientists and IT professionals can use to run simulations before building and deploying genuine devices.

FREMONT, CA: Digital twins (DT) serve the same purpose for complicated machinery and processes that food tasters do for monarchs or stunt doubles do for movie stars: they avert damage to valuable assets that could otherwise occur. Having made their way to the virtual world, Duplicates save various enterprises time, money, and effort while safeguarding the health and safety of high-value resources.

A DT is a highly detailed and dynamically updated virtual counterpart of physical objects or processes used to monitor performance, simulate various situations, predict faults, and identify optimization opportunities. Unlike typical computer-aided design and engineering (CAD/CAE) models, a DT always has a unique, real-world counterpart, receives live data from it, and changes in response to the data to replicate the origin throughout its existence.

However, twinning does not occur spontaneously. This technique entails various components cooperating to create a unified system.

A DT system comprises hardware and software components connected by middleware for data management.

Hardware components: The essential technology behind DTs is the Internet of Things (IoT) sensors, which initiate information exchange between assets and their software representation. Additionally, the hardware component comprises actuators that transform digital data into mechanical movements and network equipment like routers, edge servers, and IoT gateways.

Data management middleware: Its fundamental component is a centralized repository for data gathered from many sources. In an ideal world, the middleware platform would also handle networking, data integration, data processing, data quality control, data visualization, data modeling, and governance. Common IoT platforms and industrial (IIoT) platforms are examples of such solutions, as they frequently include pre-built tools for digital twinning.

Software components: The analytics engine is a critical component of digital twinning because it transforms raw observations into actionable business information. Machine learning models frequently fuel it. Additionally, dashboards for real-time monitoring, modeling tools, and simulation software are required pieces of a DT puzzle.

DTs and the IoT

The proliferation of IoT sensors contributes to the feasibility of DTs. Additionally, as IoT devices get more polished, digital-twin situations can encompass smaller, less complex products, providing businesses with additional benefits.

Based on varied data, DTs can be utilized to anticipate various outcomes. This is analogous to the run-the-simulation scenario frequently shown in science fiction films, in which a conceivable situation is demonstrated within a digital environment. When combined with additional software and data analytics, DTs can frequently improve an IoT deployment for optimal efficiency and assist designers in determining where things should go or how they should operate before they are physically installed.

The more accurately a DT can replicate a physical product, the more probable it will discover efficiencies and other benefits. For example, in manufacturing, where heavily instrumented devices are used, DTs can be used to model how the devices have performed through time, assisting in forecasting future performance and possible failure.

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