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What is GE Digital Twin?

Employing advanced analytics and machine learning methodologies is vital for reducing operational costs and alleviating risks. Digital twins, a key element of the digital transformation journey, offer exact virtual models of physical assets, systems, and objects that aim to improve productivity, streamline processes, and boost profitability. Generally, a digital twin is seen as a software representation of a tangible asset or system designed to identify, prevent, forecast, and enhance operations through real-time data analysis, ultimately providing considerable advantages for businesses. At GE Digital, we focus on utilizing digital twin software to support our clients across three essential areas: Asset, Network, and Process. By adeptly overseeing, simulating, and controlling an asset or process, organizations can greatly improve system performance. Moreover, it is imperative to prioritize the health and safety of employees and the environment while pursuing business objectives, which entails reducing incidents associated with assets and processes and avoiding unexpected downtimes, thus nurturing a more robust operational structure. The adoption of digital twin technology not only boosts efficiency but also opens doors for innovation across numerous industries. As organizations embrace this technology, they position themselves for sustained growth and competitive advantage in an ever-evolving marketplace.

What is DC-E DigitalClone for Engineering?

DigitalClone® for Engineering stands out as the sole software that seamlessly combines various scales of analysis within a unified platform. Recognized globally as the premier tool for predicting gearbox reliability, DC-E excels not only in its modeling and analysis capabilities specific to gearboxes and gear/bearing interactions but also uniquely incorporates fatigue life modeling through advanced, physics-based methodologies (US Patent 10474772B2). By enabling the creation of a digital twin for gearboxes, DC-E encompasses every phase of an asset's lifecycle—from the optimization of design and manufacturing processes to the selection of suppliers, followed by thorough root cause analysis of failures and condition-based maintenance along with prognostics. This innovative computational environment significantly decreases both the time and costs associated with launching new designs and ensuring their long-term maintenance, ultimately enhancing operational efficiency. Moreover, it empowers engineers to make informed decisions at every stage, leading to improved performance and reliability.

Media

Media

Integrations Supported

APERIO DataWise

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Upon request
Depending on AWS based non-government and government operations
Free Version

Supported Platforms

Windows
Linux

Supported Platforms

SaaS

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

GE Digital

Date Founded

2015

Company Location

United States

Company Website

www.ge.com/digital/applications/digital-twin

Company Facts

Organization Name

Sentient Science Corporation

Date Founded

2001

Company Location

United States

Company Website

sentientscience.com

Categories and Features

Digital Twin

Not specified

Simulation

Not specified

Categories and Features

Digital Twin

Not specified

Engineering

2D Drawing
3D Modeling
Design Analysis
Mechanical Engineering

Root Cause Analysis

Not specified

Simulation

Not specified

Statistical Analysis

Statistical Process Control
Statistical Simulation

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