Recommended
More Related Content
Similar to UReason - Webinar Digital Twins in Data-Driven Maintenance
Similar to UReason - Webinar Digital Twins in Data-Driven Maintenance (20)
Recently uploaded
Recently uploaded (20)
UReason - Webinar Digital Twins in Data-Driven Maintenance
- 1. Digital Twins for Data Driven Maintenance? Insights in (20) minutes + Q&A Speaker : Jules Oudmans @UReason : Responsible for APM Deliveries Background : Applied Physics, Mechanical Engineering & Computer Science AI/Ind 4.0 : >25 Years/ > 8 Years Motto : “Digital, What Else?“
- 2. About Us Software APM: On-Device, Edge, Cloud 20+ Years in Business Monitoring and optimization of components, assets and processes with data Industry Knowhow Operations, Maintenance, Data Architecture, OT & IT
- 3. Software: APM Studio Software Platform for developing and deploying Industry 4.0 applications In a low/no code environment Condition Based Monitoring Predictive Maintenance Digital Twin Advanced Alarm Management Low/No Code High accuracy by combining casual models with Machine Learning Scalable to 1000’s of devices As Platform Asset Owners As Micro Service OEMs & Skid Providers
- 4. What do we do every day: Use Cases & Business Cases Model Development Data Collection Hardware & APM Software
- 5. Projects Manufacturing Asset Owners OEM Skid Builders OEMs (40% of our Projects) (60% of our Projects) APM Studio Industry 4.0 Applications
- 6. Digital Twins .. Everywhere .. But What is it? “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 “The concept is exciting, absolutely, but more complex than one can be led to believe. Today there is a naiveté in many companies about the cost and time aspects.” – Marc Halpern, Gartner Analyst
- 7. Digital Twins Standard! digital twin <manufacturing> fit for purpose digital representation (3.2.2) of an observable manufacturing element with synchronization between the element and its digital representation digital representation <manufacturing> data element representing a set of properties of an observable manufacturing element (3.2.5) observable manufacturing element OME item that has an observable physical presence or operation in manufacturing. Note 1 to entry: Observable manufacturing elements include personnel, equipment, material, process, facility, environment, product, and supporting document.
- 8. A digital twin is a digital representation of a real-world entity or system. The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction. Data from multiple digital twins can be aggregated for a composite view across a number of real-world entities, such as a power plant or a city, and their related processes. The Digital Twin Source: Gartner Inputs Real World Objects Processing – Simulate/Predict Outputs (To act upon)
- 9. Types of Digital Twins Process Twin System/Unit Twin Asset Twin Component Twin Manufacturing Process Crude Unit, Cooling Unit , … Turbine, Motor, Valve … Bearing, Piston, Axle, … Field Services Management Designers Product Managers Marketing/Sales
- 10. Elements of a Digital Twin 1) Physical Equipment 2) Twin Model (Data) 3) Knowledge 4) Analytics
- 11. Example – FOCUS-ON – Asset/Component Twin Inspection Notification Approval Work Prep. Scheduling Execution Closeout Planned Maint Breakdown PdM / CbM Data Based Automate Automate: NE107 Events straight to your CMMS Data Based: CbM and PdM on the basis of the data in the device (APM Inside)
- 12. Example – KUKA- System Unit Twin Problem KUKA’s production cell with HELLER Milling Centers is running non-stop, including the weekends without personnel. During the weekends some of the tools run to the end of their lifecycle and the whole production stops until Monday morning when work-floor personnel comes back. This results in lost production and lost profits. Solution UReason developed an algorithmic tool change recommender inside APM Studio. The recommender system uses the remaining tool lifecycle, available pieces and production programs to advise the work-floor personnel what concrete actions to take regarding tool change to ensure the most optimal production over the weekend. https://www.ureason.com/resources/ureason-supports- kuka-towards-more-autonomous-production/
- 13. Example – Airborne – Process Twin Problem Airborne’s production cell produces laminates that have very tight quality specs – too large gap width results in product loss and lost profits. Solution UReason deployed an APM-Studio solution that provides early insights into gap-width using ML models developed by Vortech. The system predicts upcoming gap-width such that operations/control can intervene https://www.ureason.com/resources/whitepaper- machine-learning-in-de-fabriek/
- 14. Qualifying Criteria for Digital Twins Criteria to consider for Digital Twins and Data Driven Maintenance: 1. Data from field level can be extracted and enhanced to provide further insights higher in the chain. 2. The problem must be support business case, meaning there should be a target or an outcome to predict/calculate that is of value to operations. 3. Preferably the problem should have a record of the operational history of the equipment that contains both good and bad outcomes. 4. The business should have domain experts who have a clear understanding of the problem.
- 15. Steps to Realize Digital Twins
- 16. Summary Source: Reddit MythBusters 1. Digital Twins are not a Myth! 2. Industry Standard: ISO 23247 3. Different Levels of Digital Twins with different use-cases 4. Elements of a Digital Twin 5. Criteria before starting https://www.ureason.com/resources/article-how- creating-a-digital-twin-helps-plants-run-better/
- 17. Valve App
Editor's Notes
- Hello everyone welcome to this webinar hosted by UReason. In this webinar we will look at how Digital Twins can help you in the world of Data Driven Maintenance. This short webinar has four parts: First, I will briefly introduce the company UReason After this I will go into detail on the different types of Digital Twins, the elements that make a Digital Twin and criteria for Digital Twins to be used in Data Driven maintenance initiatives Then we have a look at some of the examples of Digital Twins that we have been active in And this is followed by a Q&A session I am Jules Oudmans presenting to you today I have a background in AI starting in the nineties and have been involved many times in the past 25 years in prognostic and predictive programs that ensure asset integrity for critical assets and critical processes. I have a mixed background in physics, mechanical engineering and computer science .. And my motto is alike the famous coffee one … “Digital, What Else”
- I work at UReason, a software company, that provides solutions for real-time condition based and predictive maintenance and I help our customers daily to use our software – from data analysis to the set-up of applications and solutions that monitor important assets and processes. At UReason we combine our domain expertise and software knowledge with our customers, and I help them from data to solutions. We have offices in Rotterdam, which we see here in the pictures, and Wokingham in the UK. Our customers are predominantly in the manufacturing and process industry and the majority of them are located in Western Europe and North Americas.
- Our software, APM Studio, is used at different levels in the automation pyramid Embedded – with OEMS – monitoring Faults and Risks in ‘isolation’ to the asset. An Asset can be instrumentation an actuator or a pump, compressor, filter et cetera At the Edge … processing asset data of one or multiple assets to run condition monitoring and predictive applications near to where the data is generated. AND we also work at Level-2/Level-3 where APM is used to monitor faults in relationship to the process, deployed/running on on-premise compute When deployed at Level-4 and Level 5 APM-Studio is used for optimizing the maintenance costs and planning associated to an asset base supporting a process.
- UReason is active in real-time condition monitoring, predictive and prescriptive maintenance. Our field of operation is from helping customers to insights into data to helping Asset Owners, OEMs and maintenance service organisations with data driven maintenance solutions. Often, we start together with our customers to define the business cases and use-cases to focus on, followed by data collection, model development and deploying the solutions into the existing OT and IT landscape.
- We work and deploy our software APM Studio for Manufacturing companies .. This is about 40% of our business and we work a lot with OEMs and Skid Builders. For OEMs Their Focus: is to Maintain margin, provide Data integration, New services/business models and Staying relevant for the customer. They typically focus on The Value of the Asset. For Asset Owners Their Focus is to Lower (energy) costs, Reduce planned maintenance, Reduce reactive maintenance, Stretch asset life, Balance risks and Optimize planning For Asset Owners it is all about The Value of the Asset Supporting the Process.
- So in today’s webinar I want to introduce the topic of Digital Twins. Digital Twins get a lot of attention in the media and I would like to break down in this webinar what a Digital Twin means and how to apply it in the world of Data Driven Maintenance. There is a lot of unclarity in my opinion about what a Digital Twin is/what they are .. BUT ExxonMobil released a pretty nice infographic that explains it in the right way. In this Venn diagram we see .. (read out/explain diagram)
- And to top the ExxonMobil Venn Diagram of a Digital Twin there is a ISO Standard that describes the concepts, limitations, boundaries and requirements as well as providing reference models and different views (functional information and network view) I mention this cause most of the customers we work with are unaware of this standardization effort that can help you create some clarity!
- Even though there is a ISO standard I like the Gartner definition better .. Because it is more concrete Digital Twin technology brings an exact replica in digital format, so in software format, of a process, a product, or a service. Basically, it takes real-world data about a physical object or system as inputs, and produces outputs in the form of predictions or simulations of how that physical object or system will be affected by those inputs. The most common use case for digital twins are: Visualization of products in use, by real users, in real-time Troubleshooting of remote or inaccessible equipment Managing complexities and linkage within systems-of-systems Connecting disparate systems and promoting traceability
- There are different types or levels of Digital Twins and these have different Use Case Scenarios: The Component Twin for example a Bearing, Piston, Axle : Can support field services/technicians to continuously monitor and offer predictive maintenance insights while reducing equipment downtime (planned and unplanned) and enable service-based business models. An Asset Twin for example a Turbine, Motor, Control Valve: Can support product management, sales to gather knowledge on customer’s preferences and actual usage of their product and provide new service business models to drive revenue. System / Unit Twin For example a Crude or Reverse Osmosis Unit: Helps product designers, architects, and engineers to improve future product versions and engineering models to optimize product performance and efficiency, accelerating time-to-market. Process Twin For example a Composite Manufacturing Process: Helps management to get new operational data feeds into production and planning models thus paving way for strategic insights, recommendations, and road maps.
- A Digital Twin consists of 4 main elements: … The Physical equipment - the actual equipment item or items that we are interested in creating a twin for. … The Twin Model – The software model consisting of a hierarchy of systems, sub-assemblies, and components that describe the twin and its characteristics enriched by asset, operational, historical, and context data. … Knowledge - Data sources that feed the twin with operational settings, domain expertise, historical data, and industry best practices. … Analytics – Simulation and/or Machine Learning models these can be physics-based models, statistical models, and machine learning/AI models to help describe, predict and prescribe the behavior (current and future) of the asset, system, or process. In the screenshots in this slide I am showing various parts of our software, APM Studio, that is used to set-up digital twins
- Now let us look at some examples of Digital Twins .. Here we have the FOCUS-1 device .. It has onboard an asset DT build in our software APM Studio.. Allowing Cruise Control in Asset Management by Providing embedded diagnostics Embedded Soft Sensors for critical measurements – true digital software twins .. And It is capable of reporting what process conditions are taking place (such as cavitation/flashing) and what maintenance field support is required.
- An example at the Unit level is one at KUKA where our software APM Studio provides recommendations in remaining useful lifetime of tools in the production center in relation to the jobs the center has to fulfill. Digital data twins and AI models support the site to have less disruptions during unmanned production periods and increases the output of the cells in the Augsburg facility.
- A process Twin example is one at Airborne .. Here AI models that predict the gap-width of the laminates that are produced help operations/control to make in-line adjustments reducing scrap and improving overall output.
- Now … Not all use cases or business problems can be effectively solved by predictive maintenance using a Digital Twin. They are not a cure to all illnesses. The important qualifying criteria that you need to consider during use case qualification for Digital Twin projects are: An obvious one; Data from field level can be extracted and enhanced to provide further insights higher in the chain. The problem must be support business case, meaning there should be a target or an outcome to predict/calculate that is of value to operations. Preferably the problem should have a record of the operational history of the equipment that contains both good and bad outcomes. Finally, the business should have domain experts who have a clear understanding of the problem.
- The steps to realize Digital Twins are quite logical. Think before you begin .. It is not about trying new technology but selecting a Digital Twin that provides value to your organization/business and or customers. Value can be reduction of planned maintenance, creating longer preventive maintenance time horizons, knowing hourly/daily what the risk and associated cost of operation is etc cetera The next step is to build and realize the Digital Twin. What we saw in the previous slides is that you need access to data streams (access to the data from the physical asset), historical data, knowledge/simulation models and criteria – for example I want to now where on the PF curve may asset currently is. Validation is off course key (hence why you need historical data). Once successfully validated you can deploy and embed the DT in your processes, but you do need to monitor its performance/deviations and embed the life-cycle management of your Digital Twins in your organisation.
- To summarize … DT are not a myth You have a standard to guide you .. In terminology/structure There are different levels of DTs .. And you do not need to start with a full DT of your process(es) Before you start reflect on the criteria I shared with you Als have a look at the latest article on our website!!
- Let me point you out to you that we have a DT model also appified .. In the Valve app which allows you to get easy insights into control valve performance and make the right decision in asset management/planning .. Let me know if you need additional information on this!
- Ok the session is open to questions .. Let me check with Carlos what questions already came in … We have critical pumps in redundant set-up, is there a value in having digital twins for this? Can the data I have in my data historian can be used as a basis to develop soft sensors? Can a component Digital Twin give me indications of remaining useful lifetimes? Ok that was the last question, thank you very much from my side for listening and asking interesting questions. Here are my contact details and please note that after the webinar you will receive the slides and a link to the replay. Again we appreciate your feedback .. please leave us your feedback via the evaluation form – see the link in the chat window. I wish you a nice day and maybe we'll see you in one of our next webinars.