Developing an Embedded Digital Twin for HVAC Device Diagnostics
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1 Developing an Embedded Digital Twin for HVAC Device Diagnostics Gianluca Bacchiega R&D manager at I.R.S. ni.com
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 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 Ganesh Bell, chief digital officer and general manager of Software & Analytics at GE Power & Water Digital twin Explosion: billions of twins in next five years
3 an Engineering Company
4 1 Digital twin: what? Embedded digital twin for HVAC diagnostic A twin using model technology 4.0 Value and ROI of digital twins 5 Conclusion
5 Digital twin: what? ni.com
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 chiller digital twin It s more than a model
8 A digital twin is a real-time digital replica of a physical device. Sensors Model digital twin Sensors
9 A simple digital replica? History Log the device history digital twin Future Forecast device future Sensors
10 A bridge between the physical and digital world Physical device Data acquisition Sensors
11 A bridge between the physical and digital world Monitoring Machine Learning & Models Big data
12 A bridge between the physical and digital world Digital Twin Physical devices Data acquisition Monitoring Machine Learning & Models Sensors Big data
13 Embedded Digital Twin for HVAC diagnostic ni.com
14 We developed an Embedded Digital Twin Physical devices Data acquisition Monitoring Machine Learning & Models Sensors embedded digital twin Big data Sensors
15 for HVAC Device Diagnostics Physical devices Data acquisition Monitoring Machine Learning & Models Sensors embedded digital twin Big data Sensors Fault Detection and Diagnosis
16 From monitoring to embedded digital twin 1. Lifelong Device history 2. Real time model computed virtual sensor 3. Real Time predictive alert
17 A twin using model technology 4.0 ni.com
18 Model technology 4.0 Physical Model Machine learning Fluid properties Components Phenomena DAQ correction Compressor Heat transfer Heat exchangers Mass transfer Fans embedded digital twin
19 HVAC Physical Model Physical Model Fluid properties Components Phenomena DAQ correction The phenomenological model, based on equations, Compressor Heat exchangers Heat transfer Mass transfer embedded digital twin can identify the causes of a possible malfunction Fans
20 Machine learning Unsupervised data Supervised data Feature extraction Machine learning algorithm The machine learning approach needs no detailed knowledge about machine operation. New data Predictive model It needs a learning phase to be able to predict the system performance.
21 Diagnostic detail and easy implementation Sensor Data Physical Physical Model Model Machine Learning Test system Fluid properties Components Phenomena DAQ correction Compressor Heat transfer Heat exchangers Mass transfer Fans
22 Merging model technology using NI platform LabVIEW Machine Learning Toolkit embedded digital twin
23 Value and ROI of digital twins ni.com
24 A bridge between the physical and digital world with Value and ROI embedded Understand using a learning model Enhance add virtual sensors Warn on health & efficiency Maintain & log the entire life of an asset Predict Failure and Optimize Digital Twin
25 Value and ROI of digital twins Maintain & log the entire life of an asset
26 Value and ROI of digital twins Understand by learning model #1 #2 #3 #4... #5 #N
27 Value and ROI of digital twins Enhance add virtual sensors Temperature Pressure Flow Efficiency & Power consumption Thermodynamic cycle point
28 Value and ROI of digital twins Warn on health & efficiency Digital twin
29 Value and ROI of digital twins Predict failure Optimize Predict Failure and Optimize
30 Conclusion ni.com
31 Conclusion : smart monitoring Machine level Plant level Company level Customer level Sensors Edge computing & gateway Cloud & Analytics Smart client & Augmented reality Measure Acquire, control & model Aggregate, understand & publish View
32 Conclusion: Artificial Intelligence and physical model Intelligence at the edge Raw Data Data Mining Predictive model RT Test system System training decided by the operator Machine Learning
33 Implement digital twin using NI platform and partner like n
34 Thank you for your attention. any question or inquiry
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