Agile Industrial Analytics Successfully introduce analytics services in the machinery industry Dr. Christian Schlögel Chief Digital Officer KUKA AG Dr.-Ing. Babak Farrokhzad Senior Manager Data Analytics Device Insight GmbH Seite: 2
IIoT Organization & Investment Strategy of KUKA Founded Strategic Investment Acquired Industry 4.0 Accelerator IIoT platform for manufacturing, logistics & machine builder Vertical & horizontal applications Data Science & AI competence IoT platform CENTERSIGHT NG IoT project expertise, 1 Mln. connected devices Broad connectivity experience (connected products house) 3D simulation engine Part of digital twin offering Simulation competence center Online/Offline capabilities Seite: 3
The Analytics Challenge: Manage New Technology & Business Model at once Set-up of new process, e.g. flexible planning of service depending on incoming maintenance time forecasts BUSINESS MODEL Machine learning technologies: logistic regression, ARIMA, neural networks Sustainable earning models Main goal: Minimize risk and complexity KNOWN NEW Proactive maintenance time forecast based on development of mileage Reactive service Proactive maintenance time forecast based on wear analysis Success factor: Take one step at a time KNOWN TECHNOLOGY NEW Introduce IoT in smaller, agile steps Seite: 4
KUKA s Analytics Approach Supports ANY Level of Process Maturity and Automation Using simple and medium weight machine learning algorithms to forecast maintenance times Using statistical methods for automatic detection of upcoming anomalies Rule Engine integrated in IIoT platform to enable alarming and notification functions Transparent views immediately make needs for action visible Connected assets to edge cloud and data acquisition via OPC/UA protocols Seite: 5
Increasing Productivity with Industry 4.0: Typical Case from KUKA s Production Automatic manufacturing of robot parts KR500 robot picks up cast component Gripper from Zimmer Milling in Heller machining centers deburring in outer position unload by next robot Potential: 4 % productivity increase only by IoT-based increase of availability (first step of ladder) Typical production cells like that of our customers: High utilization of intelligent components, e.g. robots Autonomous production with complex process Many assets from different suppliers Big part variants Seite: 6
Machine Layer Edge Layer Cloud Layer Architecture KUKA Smart Production Connectivity-Box Nebbiolo Box Agent OPC UA OPC UA Server on Softing Gateway S7 (Master-Controll of the cell) Altran Senseforge KRC U U U Heller 6000 Heller 8000 Gripper station Linear axis Turntables Zimmer Gripper Sensors KRC: Kuka Robot Control, EthCAD: EtherCAD Seite: 7
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Intuitive Rule Engine for the Operators with Alarming and Notification Functions USE CASE APPLICATION ALGORITHM FAULT DETECTION METAL JOINING EXPERT RULE ENGINE Shaded area shows time when event is active Seite: 9
KUKA Robot Asset View KR 500 Asset Information (master data) Asset Maintenance Status Analytics and Torque Real Time Production Relevant Data Seite: 10
Real Maintenance Time Forecast Time Real Maintenance Time = Forecast Time Mileage-based Estimation of Maintenance Time Based on Top-Level USE CASE PROACTIVE MAINTENANCE APPLICATION ROTATING EQUIPMENT ALGORITHM LINREG Experimental analysis of available data to check suitability of algorithms THRESHOLD THRESHOLD dec may dec may After 30% of time Measured = 0.945 Estimated = 1.127 Error = 19% After 60% of time Measured = 0.945 Estimated = 0,966 Error = 2% Seite: 11
Extrapolation with ML Using Generalized Additive Models is More Precise Due to Extraction of Trend and Seasonality A=Availability; P=Performance; Q=Quality, OEE potential w.r.t. data from Nov 2017. Learning Window 44 days, ex-post comparison of results. Warning one week in advance Seite: 12
Examples for Systematic Identification of Machine Learning and Statistical Models Use Case Examples Models & Tools Steps of Ladder Predictive Maintenance Condition-based maintenance for Gripper (Cycle-time), Robot (Runtime), etc. FORECAST Maintenance Time Linear Regression Generalized Additive Models Anomaly Detection Number of strokes and phase difference between voltage in stator and rotor CLASSIFICATION Outlier Detection T-Test Support Vector Machine K-Means/kNN Quality Assurance for Machines/Parts Is the part manufactured under the measured parameter set good or scrap? CLASSIFICATION Yield Analysis Tow-Class Boosted Decision Tree Logistic Regression Neural Network Seite: 13
Most Prevalent ML-Algorithms as Researched by KUKA s Data Science Experts Start with those that are Easy to Implement TYPICAL USE CASE (MACHINERY) ILLUSTRATIVE BUSINESS QUESTION(S) ALGORITHM Prediction (numeric values) Prediction status of machine/part Evaluating status of machine/part When is the next maintenance cycle due for every machine in the line? Which components will be subject service? With what probability? How will the energy consumption of the site be in.? Is the part manufactured under the measured parameter set good or scrap? Does the measured parameter set show that the machine is defect? Are the process parameters within "normal range"? Is/are the values measured simple "outliers" or is there any "urgency for action"? Linear Regression Generalized Addtive Decision Tree Random Forest Gradient Boosting Tree Naive Bayes Logistic Regression Linear SVM Kernel SVM Neural Network Anomaly Detection Neural Network Detection of similarities between data point curves What events do simultaneously occur in different data points? What patterns in the data points do correlate? Affinity analysis Time Warping Reduction of Complexity What are the top 5 parameters (out 500) determining produced parts quality? Principal Component Analysis Singular Value Decomposition Clustering of data points What are the clusters of values in the data points? Which one are important? Source: KUKA, Device Insight, Analysis of Customer Projects, inquiries and literature K-Means K nearest neighbors Gaussian Mixture Model DB SCAN Seite: 14
SOUTHBOUND API MQTT HTTPS AMQP NORTHBOUND API HTTPS WEBSOCKETS Algorithms Are Run by an Industrial Analytics Service and Use Python Machine Learning Library CENTERSIGHT NG APPS PARTNER APPS INDUSTRIE AUTOMATION ASSET VIEWER/ADMIN DEVICE MANAGEMENT STAT. APP IND. ANALYTICS CONF. CM CONFIG APP 1 Operator Model Training TELEMATIK FLOTTEN- MANAGEMENT Automatic Data Scientist ENERGIE & SMART CITY CENTERSIGHT EDGE CONNECTORS DATA PROCESSORS COMMUNICATION SERVICE NG CORE CENTERSIGHT NG SERVICES EVENT ENGINE (CM) NG DISTRIBUTED STREAMING INDUSTRIAL ANALYTICS SERVICE 2 TIMESERIES EXPORT PARAMETRISATION FINDINGS IMPORT 4 GUIDED ANALYTICS FRAMEWORK TRAINED MODEL/Python Skript Automatic 3 TRANSMIT BUFFER TIME SPACE EVENT LOG Seite: 15
Learning from Our Own Self-Medication Experience and Customer Projects Follow a "ladder approach": One step technology, one step business processes Start with simple connectivity & alarming: These often give you substantial productivity increase of up to 5 % Ask the operators with which are the key use cases: They always gave us the best input Be hard-nosed on using commercial impact of use cases as a selection criterion Use data science and to get the next boost on OEE, know-how and experience Be cautious when somebody recommends the latest, "most hip" ML algorithm When selecting a partners check their approach concerning technical complexity, leadership issues and project risk assessment Seite: 16
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