Digital Twins Technology – IDA Mechanical & IPU 17:00 – 17:05 Welcome & Introductions 17:05 – 17:20 Introducing IPU & Digital Twin Technology – Søren Merit, CEO 17:20 – 17:45 Digital Twins for Condition Based Maintenance of Refrigeration Containers – Ragnar Ingi Jónsson, Specialist Engineer 17:45 – 18:15 Break – Sandwich & Networking 18:15 – 18:35 Model-in-Loop Software Development for Automation of Heavy Duty Machinery – Kevin Rice, Senior R&D Engineer 18:35 – 18:55 Virtual Models for Product Analysis and Manufacturing Processes – Nikolas Aulin Paldan, Specialist Engineer 18:55 – 19:00 Final Remarks & Questions 2019-03-26 Digital Twins Technology - IDA Mechanical & IPU 1
Introducing IPU & Digital Twin Technology Søren Merit CEO at IPU, Technology Driven Business Innovation, M.Sc., B.Com. (+45) 40 90 46 30 sme@ipu.dk
We are a spin-off of the Technical University of Denmark Started in 130 70% 50% 1956 million DKK in donations Technology development for of projects are we working by 4 DTU professors supporting DTU research industry with DTU researchers Independent commercial foundation Purpose to facilitate use of new technology in Danish 30% industry Research projects 2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, sme@ipu.dk
We Develop Solutions to Complex Technology Challenges • Specialists in multi-disciplinary product- and manufacturing technology development • Team of international specialists Product and Discovery Basic Research Applied Research Manufacturing Production Development • Technology Search • Proof of Concept • Feasibility Study We help our clients speed up development • Test setup … and reduce technical risks • Data analysis … and manufacturing uncertainties • Prototyping & development • Modelling & Simulations • Digital Twin 2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, sme@ipu.dk
IPU Key Expertises Advanced materials Thermodynamics Autonomous Physical systems and surfaces and energy systems and modelling automation Materials Types and Choices Software and simulation Software & Algorithm Digital twins Surface Treatment Modelling of Cooling Development Fault detection Corrosion and Wear Processes Condition-Based Monitoring FEM analysis Protection Energy Optimisation System Analysis Machine learning We can help you… We can help you… We can help you… We can help you… developing and optimizing materials analyzing thermodynamic and heat modelling, analyzing and developing digital models of and surfaces processes for specific transfer processes and their developing complex autonomous physical system in order to perform purposes, including analyzing components. systems, robotics and automation simulations in a fast and safe problems and malfunctions. of systems and processes. environment. We optimize system performance Our core strengths are metallic and and efficiency using tailored Combining development of We develop digital twins, perform polymer materials as well as simulation models and the latest mechanical design and hardware big data analyses, decision electroplating and corrosion R&D expertise. with control systems and software algorithms (AI), machine learning and visual pattern recognition 2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, sme@ipu.dk 5
How We Work with Complex Systems Understand business case and process Multi physics Prototyping modelling and tests Understand physics Data Machine analysis Learning 2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, sme@ipu.dk 6
We Develop Solutions to Complex Technology Challenges IPU has developed a software solution offering predictive maintenance and conditions-based fault detection of refrigerated containers enabling significant savings in costs related to physical inspection. IPU has developed an autonomous IPU has developed an systems solution for heavy duty automation concept for construction machinery. IPU developed the new ESO telescope, automation control system, retrofit using a safe chemical hardware components, develop control cleaning process, that software and operator user interface. strips the mirror coating Digital twin based hardware and software during planned in the loop development maintenance, without altering the fragile mirror substrate. 2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, sme@ipu.dk 7
Digital Twin Technology
Welcome to the Most Hyped Technology! Gartner’s Hype Cycle, August 2018 2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, sme@ipu.dk
What is a Digital Twin? Digital model of the elements and ..applied in development dynamics of how a product, process or service operates ..and in operations 2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, sme@ipu.dk 10
What is Digital Twin? Development Operations Digital model of the elements and dynamics of how a product, process or • CAD interacting with multi- • Installation: Calibrating and service operates adjusting physical simulations • Developing and testing • Monitoring: Comparing software (Model-in-Loop) sensor data with simulation results • Developing and testing • Optimizing: Adjusting components system for wear and (Hardware-in-loop) external conditions 2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, sme@ipu.dk 11
Why use Digital Twins Automotive Benefits of digital twins • Faster insights – Fail fast succeed faster • Cheaper and faster tests • Feasible to explore extreme conditions • Understand dynamics better Aerospace • Ability to predict and adjust – during operations • Faster update with minimal stops in operations 2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, sme@ipu.dk 12
Digital Twins for Condition-Based Maintenance of Refrigeration Containers Ragnar Ingi Jónsson Specialist Engineer at IPU, Physical System Modelling & Conditions-Based Monitoring, M.Sc., Ph.D. (+45) 45 25 41 86 rij@ipu.dk
Maersk Motivation & Goals • Remote Container Management (RCM) • Connectivity and transparency – being in control • Improved customer experience – documentation • Monetary savings – maintenance and operation • Vast funds spent on pre-trip inspections (PTI) approx. US$ 750 yearly, per unit. How can we improve the efficiency of the reefer maintenance operations, cargo safety and energy consumption? 2019-03-26 Digital Twins for Condition-Based Maintenance of Refrigeration Containers, Ragnar Ingi Jónsson, rij@ipu.dk 14
Timeline & Overview • 2010 IPU/Maersk collaboration on Reefer Alarm Prediction System (RAPS/ePTI) of the RCM system • 2012 Agreement with Ericsson and AT&T for hardware and data infrastructure. • Satellite communication installed on 400 vessels • Local GSM communication between container and vessels • 2015 RCM system launch may 1 st . • 2018 IPU to update RAPS/ePTI with new reefer models, detections updates and other features. 2019-03-26 Digital Twins for Condition-Based Maintenance of Refrigeration Containers, Ragnar Ingi Jónsson, rij@ipu.dk 15
RAPS / ePTI – Step-by-Step 1. Sensor readings are collected and saved on each reefer. 2. Reefer data is send to vessel via on-board local GSM or commercial GSM while in land 3. Satellite communication sends reefer data to head quarters 4. Individual reefer data is processed through simulation models and fault detection algorithms. 5. Alarms and warnings are reviewed and appropriate actions taken service ordered if needed. 2019-03-26 Digital Twins for Condition-Based Maintenance of Refrigeration Containers, Ragnar Ingi Jónsson, rij@ipu.dk 16
Reefer Models for Fault Detection • Model requirements • Fast calculation models • Include all major properties • Basic refrigeration system • Heat uptake, heat release, power consumption • High and low pressure parts • Internal temperatures • Other temperatures included (ambient, cooling water, reefer, set point) • The general refrigeration system model • Refrigeration type determines, compressor type, whether there is e.g. internal heat exchange, economizer etc. 2019-03-26 Digital Twins for Condition-Based Maintenance of Refrigeration Containers, Ragnar Ingi Jónsson, rij@ipu.dk 17
Fault Detection Algorithm Overview Fault Normal operation measured data Reefer Data time stamp operational data o ther… setpoint ambient conditions measured data cargo info not used in etc. Model model simulated values Residual component-wise Normal operation (difference) Fault residual data Normal Faulty Statistical Behavior Detection Behavior Detection Threshold Threshold Comparison Detection Results 2019-03-26 Digital Twins for Condition-Based Maintenance of Refrigeration Containers, Ragnar Ingi Jónsson, rij@ipu.dk 18
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