From Zero to One with Zero.One. Bruno Bülow Product & Sales Manager ARVOS GmbH SCHMIDTSCHE SCHACK

From Zero to One with Zero.One Bruno Bülow Product & Sales Manager ARVOS GmbH SCHMIDTSCHE SCHACK Agenda Introduction Current Situation Introduction to Zero.One Predictive Maintenance in Theory and Practice

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From Zero to One with Zero.One Bruno Bülow Product & Sales Manager ARVOS GmbH SCHMIDTSCHE SCHACK Agenda Introduction Current Situation Introduction to Zero.One Predictive Maintenance in Theory and Practice Machine Learning Contact 2 DECISION I have repaired this item many times. Maybe it is better to buy a new one. MATERIAL Hi Joe, where is the needed material? The plant has been down for 5 days and I can t start with the repairing service. PROFIT Hey boss, this downtime takes at least 14 days and will sum up to more than 4 million dollars. PEOPLE There were so many difficulties upfront to get our onsite service organized. Why can this not be faster? 3 3 Maintenance Schedule Scheduled maintenance with longer downtime through inspection and material order 28 days 4 5 6 Customer Benefits Customer benefits based on function, availability and results 1 Today 2 Tomorrow 3 Near future Service Service Data Service Product Product Product 7 McKinsey Digital Compass - Value Drivers and Digital Levers Services/ aftersales Asset utilizatio n Remote Monitoring Predictive Maintenance Augmented Reality Time to market Supply chain mgmt Quality Labour Inventori es Early indication of problems Timely corrections Prioritization of resources 30 50% reduction of total plant downtime 8 THE DIGITAL SERVICE PLATFORM SCHMIDTSCHE SCHACK connects existing plant equipment with the Internet of Things (IoT) Sensor-based analysis of the components of an industrial plant Minimized Downtime Monitored threshold values Recommendations for optimal TLE utilization Easy-to-schedule service work Root Cause Wrong Torqueing sequence Anatomy of Heat Exchanger Failure Flange Leakage Failure Mechanism Equipment Impact Economical Impact Loss of Production Environmental Impact Wrong Torques Insufficient Torqueing Erosion of sealing surface Emergency Shut Down Waste of Energy Insufficient Lubricating Insufficient Sealing Flange Leakage Damaged Equipment Wrong Gasket Type Repair Costs Safety Hazard Wrong Gasket Mounting Damage of sealing surface Fire - Possible overheating Fire in an explosive environment 10 How Does Zero.One Work? Special sensors developed by SCHMIDTSCHE SCHACK Measurement of temperatures, pressures, mass flows, and material thickness Algorithm-based analysis of the current status and performance data compared with historical system data and product design information Key-information on performance, risks and prognoses visualized in customer-individual dashboards 11 Corrosion / Erosion Tube Fouling Blockage in 14 days Fouling / Blockage Propose tube cleansing or Tube change or Equipment change 12 SCS Prediction example Enabling smart Heat Exchangers Site condition Asset condition Failure Nature Fouling/ Blockage Jan. 17 Apr. 17 Jul. 17 Okt. 17 Jan. 18 Apr. 18 Jul. 18 Okt. 18 Fleet condition Errosion/ Corrosion Connectivity Sensor Connectivity Gateway Others Total Risk 13 Dashboard Examples World-wide status of plant index, asset condition and sensor health Alerts presented by different categories Per day, customer, product type Sensor and gateway status Sensor values above/ below thresholds Drill down to a specific customer, country, location, asset, gateway and sensor Alert count, plant index, asset condition and sensor health Time series analysis per sensor incl. thresholds 14 Dashboard Examples Asset Prediction Overview Based on historical data and ML algorithms Predicted time to cleaning Probability of tube blockages within the TLE Predictions on actual values vs. specified values to be developed (e.g.: (actual specified value) +/- 10% = warning alert) Customer specifc Dashboard design and elements can be adapted per customer Underlying ML algorithms to be developed per needed prediction Presentation of live and historical data MS Azure architecture to be designed per customer 15 Data Integration High Level Architecture Customer site Cloud Dashboards: life -, historical data, predictions New business models Microsoft Machine Learning Storage Live data Historical data Sensor/ gateway administration Security components Transfer Line Exchangers Syngas Coolers Carbon Black Air Preheater Process Gas Cooler Predictive Maintenance Service business development Smart data and dashboards for customers, service partners, etc. Consulting for IoT and data analytics Add. components if required Nitric Acid Boiler 16 Maintenance Schedule with Zero.One Scheduled maintenance with longer downtime through inspection and material order 28 days Extension of service intervals and avoidance of unplanned downtimes Zero.One 7 days Time savings of up to 75% for maintenance work 17 Life-cycle monitoring to detect material deterioration and wear at an early stage Preparation of availability forecasts and upcoming maintenance work Custom integration solutions Digital documentation of the TLE condition Sufficient lead time for procurement of replacement parts Provision of comprehensible rationale for maintenance decisions 18 Principle of machine learning 1. Training phase to adjust the model 2. Verification phase to evaluate model reaction to new input data 19 Predict time until de-coke Test the model # Heating Surface 85% 85% 100% 100% Load 100% 90% 90% 100% Operation time reduced regular extended regular 20 Zero.One brings value to the whole team Predictable events Longer planning horizons with greater transparency Knowledge of what is going to happen Planning of service teams only on requirement Qualification of employees on digitalization 21 Bruno Bülow Product & Sales Manager Julia Möller Head of Zero.One Distribution +49 (0)561 / (0)561 / ARVOS GmbH SCHMIDTSCHE SCHACK Ellenbacher Straße Kassel Germany 22
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