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Digital Twins Technology and Internet of Things Solutions

Digital Twins Engineering Services

Digital Twin

Digital Twin Development Services

Digital Twin is a digital illustration of a physical thing/object, and the mechanism inside the digital twin involves sensors which are attached to the physical object for collecting data which includes physical data, manufacturing data, and operational data. Digital Twins play a significant role in Industry 4.0 using IoT, Big Data Analytics, AI and Automation solutions. The industries Impacted by Digital Twins –

  • Healthcare
  • Manufacturing
  • Industrial IoT
  • Retail
  • Smart Cities
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Enabling Internet of Things (IoT)

This concept involves gathering insights from the connected systems and channels. After the collection of Data, the information is rerouted to the digital world via sensors for further analysis. Analytics Techniques are used to produce insights which is enabled with the help algorithmic functions.

The primary goal of digital twin is to identify any chances of quality and efficiency optimization, cost optimization, and to alert the physical twin for any abrupt changes.

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Digital Twins Features -

  • Improving Quality
  • Reducing Overall Warranty Cost
  • Improving Efficiency
  • Improving Performance
  • Reducing Operational Cost
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How Digital Twins Work

The Digital Twins work with the help of the Internet of Things. The process involves gathering real-time data with the help of smart components which are collected from sensors.

This data is connected with a cloud-based system for real-time monitoring and processing of data. After analyzing the data, we can perform various operations for performing business decisions.

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Digital Twins Benefits -

  • Improved Productivity
  • Lower Maintenance Costs
  • Lower Risk Rate
  • Increasing Reliability
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Digital Twins with AI

Artificial Intelligence plays a vital part in digital twins foundation.The working architecture includes two Components -

Pattern Recognition

Pattern Recognition deals with behavioral aspects of the digital asset. The technologies behind predicting the life of an asset and failure systems include Advanced Machine Learning and Transfer Learning Methods.

Learning Models

This process involves building models that can be validated, monitored, and updated continuously.

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Features of transfer learning are -

  • Mechanical detection
  • Adaptation to changing conditions
  • Lower risk of failures
  • Higher Performance

Accelerating Digital Transformation with Industry Leading AI, Big Data and Cloud Solutions

We Assist Our Clients Across Six Core Pillars

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Cloud Platform Engineering

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Enterprise DevOps Transformation

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DevSecOps and Cloud Security

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Cloud Native Software Development

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Data Platforms and Advanced Analytics

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Managed Cloud Services