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Embedded digital twin
- 1. ni.com Developing an EmbeddedDigital Twin for HVAC Device Diagnostics Gianluca Bacchiega R&D manager at I.R.S.
- 2. “Digital twins arebecoming a business imperative, covering the entire lifecycle of an asset or process and forming the foundation for connected products and services. Companies that fail to respond will be left behind.” Thomas Kaiser, SAP Senior Vice President of IoT Ganesh Bell, chief digital officer and general manager of Software & Analytics at GE Power & Water “For every physical asset in the world, we have a virtual copy running in the cloud that gets richer with every second of operational data Digital twin Explosion: billions of twins in next five years
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- 4. Digital twin: what? Embedded digital twin for HVAC diagnostic A twin using model technology 4.0 Value and ROI of digital twins Conclusion 1 2 3 4 5
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- 6. A digital twinis a real-time digital replica of a physical device.
- 7. A digital twinis a real-time digital replica of a physical device. chiller digital twin chiller It’s more than a model
- 8. ModelSensors A digital twinis a real-time digital replica of a physical device. Sensors digital twin
- 9. A simple digitalreplica ? History Log the device history Sensors digital twin Future Forecast device future
- 10. A bridge betweenthe physical and digital world Sensors Physical device Data acquisition
- 11. Big data Monitoring Machine Learning & Models Abridge between the physical and digital world
- 12. A bridge betweenthe physical and digital world Big data Digital Twin Monitoring Machine Learning & Models Sensors Physical devices Data acquisition
- 13. ni.com Embedded Digital Twinfor HVAC diagnostic
- 14. Sensors We developed anEmbedded Digital Twin … embedded digital twinSensors Physical devices Data acquisition Big data Monitoring Machine Learning & Models
- 15. Sensors … for HVACDevice Diagnostics Fault Detection and Diagnosis embedded digital twinSensors Physical devices Data acquisition Big data Monitoring Machine Learning & Models
- 16. 1. Lifelong Devicehistory 2. Real time model computed virtual sensor 3. Real Time predictive alert From monitoring to embedded digital twin
- 17. ni.com A twin usingmodel technology 4.0
- 18. Model technology 4.0 PhysicalModel Fluid properties Components Compressor Heat exchangers Fans Phenomena Heat transfer Mass transfer DAQ correction embedded digital twin Machine learning
- 19. HVAC Physical Model PhysicalModel Fluid properties Components Compressor Heat exchangers Fans Phenomena Heat transfer Mass transfer DAQ correction embedded digital twin The phenomenological model, based on equations, can identify the causes of a possible malfunction
- 20. Machine learning The machinelearning approach needs no detailed knowledge about machine operation. It needs a learning phase to be able to predict the system performance. Feature extraction Machine learning algorithm Unsupervised data New data Predictive model Supervised data
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- 22. Merging model technologyusing NI platform LabVIEW Machine Learning Toolkit embedded digital twin
- 23. ni.com Value and ROIof digital twins
- 24. A bridge betweenthe physical and digital world with Value and ROI embedded Digital Twin Maintain & log the entire life of an asset Understand using a learning model Warn on health & efficiency Predict Failure and Optimize Enhance add virtual sensors
- 25. Value and ROIof digital twins Maintain & log the entire life of an asset
- 26. Value and ROIof digital twins #1 #2 #3 #4 #5 #N ... Understand by learning model
- 27. Value and ROIof digital twins Temperature Pressure Flow Thermodynamic cycle point Enhance add virtual sensors Efficiency & Power consumption
- 28. Digital twin Value andROI of digital twins Warn on health & efficiency
- 29. Value and ROIof digital twins Predict Failure and Optimize Predict failure Optimize
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- 31. Edge computing & gateway Cloud & Analytics Smart client & Augmented reality Machine level Plant level Company level Measure Acquire, control &model Aggregate, understand & publish View Conclusion : smart monitoring Customer level Sensors
- 32. Conclusion: Artificial Intelligenceand physical model Data Mining Predictive model Raw Data RT Test system System training decided by the operator Machine Learning Intelligence at the edge
- 33. Implement digital twinusing NI platform and partner like n
- 34. [email protected] Thank you foryour attention. any question or inquiry