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Embedded digital twin
- 1. ni.com Developing an Embedded Digital Twin for HVAC Device Diagnostics Gianluca Bacchiega R&D manager at I.R.S.
- 2. “Digital twins are becoming 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
- 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
- 6. A digital twin is a real-time digital replica of a physical device.
- 7. A digital twin is a real-time digital replica of a physical device. chiller digital twin chiller It’s more than a model
- 8. ModelSensors A digital twin is a real-time digital replica of a physical device. Sensors digital twin
- 9. A simple digital replica ? History Log the device history Sensors digital twin Future Forecast device future
- 10. A bridge between the physical and digital world Sensors Physical device Data acquisition
- 11. Big data Monitoring Machine Learning & Models A bridge between the physical and digital world
- 12. A bridge between the physical and digital world Big data Digital Twin Monitoring Machine Learning & Models Sensors Physical devices Data acquisition
- 13. ni.com Embedded Digital Twin for HVAC diagnostic
- 14. Sensors We developed an Embedded Digital Twin … embedded digital twinSensors Physical devices Data acquisition Big data Monitoring Machine Learning & Models
- 15. Sensors … for HVAC Device Diagnostics Fault Detection and Diagnosis embedded digital twinSensors Physical devices Data acquisition Big data Monitoring Machine Learning & Models
- 16. 1. Lifelong Device history 2. Real time model computed virtual sensor 3. Real Time predictive alert From monitoring to embedded digital twin
- 17. ni.com A twin using model technology 4.0
- 18. Model technology 4.0 Physical Model Fluid properties Components Compressor Heat exchangers Fans Phenomena Heat transfer Mass transfer DAQ correction embedded digital twin Machine learning
- 19. HVAC Physical Model Physical Model 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 machine learning 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
- 21. Physical Model Fluid properties Components Compressor Heat exchangers Fans Phenomena Heat transfer Mass transfer DAQ correction Sensor Data Physical Model Machine Learning Test system Diagnostic detail and easy implementation
- 22. Merging model technology using NI platform LabVIEW Machine Learning Toolkit embedded digital twin
- 23. ni.com Value and ROI of digital twins
- 24. A bridge between the 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 ROI of digital twins Maintain & log the entire life of an asset
- 26. Value and ROI of digital twins #1 #2 #3 #4 #5 #N ... Understand by learning model
- 27. Value and ROI of digital twins Temperature Pressure Flow Thermodynamic cycle point Enhance add virtual sensors Efficiency & Power consumption
- 28. Digital twin Value and ROI of digital twins Warn on health & efficiency
- 29. Value and ROI of digital twins Predict Failure and Optimize Predict failure Optimize
- 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 Intelligence and 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 twin using NI platform and partner like n
- 34. info@irsweb.it Thank you for your attention. any question or inquiry