Vdsg /Craftworks Industrial-AI
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Deep Learning for Predictive Quality & Predictive Maintenance
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Vdsg /Craftworks Industrial-AI
- 1. Industrial AI Deep Learning for Predictive Maintenance, Predictive Quality & Visual Inspection
- 2. We develop prize-winning Industrial AI Simon Stiebellehner Head of AI @ craftworks Lecturer @ WU Wien & FH Wien simon.stiebellehner@craftworks.at craftworks.AI Daniel Ressi Data Scientist @ craftworks daniel.ressi@craftworks.at
- 3. Who is ? 16 Team members Biomedical Engineering User Experience Software Engineering Physics Machine Learning
- 5. craftworks.at Selection of our Clients Predictive Quality Predictive AnalyticsPredictive Analytics Predictive Maintenance Predictive Quality Predictive Quality Computer Vision Big Data InfrastructureSoftware Solution Predictive Maintenance
- 6. A Special Set of Challenges The Industry Research & Design Process Optimization & Automatization Quality Control Maintenance Consumption Logistics Customer Engagement Resource Management Security
- 7. Special Challenges require a Special Set of Methods Industrial AI Industrial Challenges Artificial Intelligence “Application of machine and deep learning methods to solve industrial challenges”
- 8. Characteristics Industrial AI ● Data ○ High-frequency ○ Big Data ○ Lack of Standardization ○ Imbalanced Classes ● Algorithms ○ No off-the-shelf solutions ○ Reliability Requirements ○ Interpretability
- 9. Industrial AI Deep Learning for Predictive Maintenance, Predictive Quality & Visual Inspection Predictive Maintenance Visual Inspection Predictive Quality
- 10. Predictive Maintenance What is it? “Predict and forecast the condition of machines to plan maintenance ahead of time” Predictive Maintenance
- 11. Why? Predictive Maintenance ● Machine Faults ○ Can stop whole production ○ High repair costs ○ Safety risk ● Predictive Maintenance 1. Machine health forecast 2. Planned maintenance schedules 3. Cost and time efficient work Predictive Maintenance Proactive
- 12. Data Predictive Maintenance Ground Truth Issue ● Start of faults ● Unnoticed faults ● Few instances of breakdowns Maintenance Records time time sensorvalue sensorvalue Predictive Maintenance ● Time series ● Machine centric ○ Temperature ○ Pressure ○ Vibration ○ Acoustic, .... Sensors
- 13. Predicting Faults in the District Heating System of Vienna
- 14. District Heating Vienna Predictive Maintenance Vienna has one of the largest district heating systems in EU Several thousand converter stations Checks and maintenance on a daily basis Every warning has to be investigated Interrupts schedules Predictive Maintenance
- 15. Goal Predictive Maintenance ● Predict faults in advance Bonus: determine exact type of fault Why? Enable more targeted maintenance schedules Reduce interruption of work caused by low-impact faults & issues Predictive Maintenance
- 16. Predictive Maintenance 2-Stage-Solution Predictive Maintenance Recurrent Neural Network (RNN) Predict fault typeGradient Boosted Decision Trees Fault probability > threshold Predicts probability of fault in the next X days (binary classification) No Fault Fault Type 1 2
- 17. Predictive Maintenance Recurrent Neural Network (RNN) Stacked LSTM Input (sensor data of last Y hours) 0 1 No Fault Fault Output (fault probability in the next X days) Predictive Maintenance
- 18. 91% of Faults can be detected 7 days in advance Predictive Maintenance Predictive Maintenance Status Predicted Faults Actual Faults Time Status: 0 = normal, 1 = Fault
- 19. Can the exact fault types be determined? Predictive Maintenance Predictive Maintenance ● several thousands unique fault types ○ Each fault type consists of 3 keywords ○ High priority types form clusters ● Fault types were clustered into coherent groups ● Selected 2 most high priority groups for classification task 98% Accuracy Fault Type Group High Prio. Group 1 High Prio. Group 2 Low Priority Group Labels
- 20. Industrial AI Deep Learning for Predictive Maintenance, Predictive Quality & Visual Inspection Predictive Maintenance Visual Inspection Predictive Quality
- 21. Visual Inspection Visual Inspection What is it? “Method of quality control using human vision”
- 22. Visual Inspection Visual Inspection Automation is Wide-Spread
- 23. Visual Inspection Visual Inspection Automated Visual Inspection is a Multi-Step Process
- 24. Visual Inspection: Detecting and Explaining Defects in Industrial Parts
- 25. Industrial Part Glued Patches Visual Inspection Visual Inspection Detecting and Explaining Defects in Industrial Parts
- 26. Defect A Defect B Defect C Visual Inspection Visual Inspection Detecting and Explaining Defects in Industrial Parts
- 27. ● Self-Learning System ● Perfect Classification in OK / Defect ● Best Possible Classification of Type of Defect Goals Visual Inspection Visual Inspection Detecting and Explaining Defects in Industrial Parts ● Inspection Capabilities ● Explanation of Model Output ● Feedback to ensure Continuous Learning
- 28. Depth Brightness Visual Inspection Visual Inspection Imaging Device captures two Types of Images
- 29. 12,000 519 18 22 327 152 Unlabelled Labelled OK Defect C Defect B Defect A Vast majority of unlabelled data is OK Visual Inspection Visual Inspection Highly Imbalanced Class Frequencies
- 30. 12,000 519 Unlabelled Labelled Challenge Very Few Labelled Images Visual Inspection Visual Inspection Data contains very few Labelled Images
- 31. Minimize Difference between Input and Output Visual Inspection Visual Inspection Deep Convolutional Autoencoder
- 32. Reconstruction Error Visual Inspection Visual Inspection Visualizing Anomalies using Reconstruction Error
- 33. Visual Inspection Visual Inspection Visualizing Anomalies using Reconstruction Error
- 34. OK Defect A Defect B Defect C Latent Space Representation Reconstruction Error Classifier 519 Labelled Visual Inspection Visual Inspection Visualizing Anomalies using Reconstruction Error
- 35. Industrial Augmented Intelligence Augmented Intelligence in Action
- 36. Industrial AI Deep Learning for Predictive Maintenance, Predictive Quality & Visual Inspection Predictive Maintenance Visual Inspection Predictive Quality
- 37. Predictive Quality What is it? “Estimating the output quality of a manufacturing process from start to finish” Predictive Quality
- 38. What? Why? Predictive Quality ● Automatic, Continuous, Proactive ○ Predict (future) quality at every process stage ○ Identify potential influences on quality ● Saves time, costs and resources ○ Prevent quality issues ■ Reduce scrap → minimize resource consumption ■ Know when a bad quality output is produced BEFORE it is produced Predictive Quality
- 39. Data Sources ● Vibration ● Temperatu re ● Pressure ● ... Sensors ● RGB ● Laser scans ● Infrared images Images ● Quality checks (ground truth) ● Shift schedules ● Maintenanc e records ● Fault logs Meta-data Predictive Quality Predictive Quality
- 40. Predictive Quality Predictive Quality Production Line Process A Process B Material Material Finished Product Product Defects: ● Defect 1 ● Defect 2 ● Defect 3 ... Process Parameters Material Meta-data F Data source Imaging E
- 41. Predictive Quality Predictive Quality Typical Problem ● Production line: ○ Sequence of processes / stages ○ Each process is sequence of steps itself ○ High volume of process data ● Random Quality checks ○ Small number of labelled data ● Goal: ○ Predict quality accurately Miss-match
- 42. Predicting Quality in Industrial Production Lines
- 43. Predictive Quality Predictive Quality Solution 1. Train LSTM Autoencoder for each process ○ Feature generator (Latent Space Representations) ○ Aggregate processes on (super-)process-level 2. Train Convolutional Autoencoder for image data 3. Concatenate features 4. Train supervised Gradient Boosting Model on labelled data 5. Use SHAP to explain influence of different stages on product quality
- 44. Predictive Quality Predictive Quality Solution Process A Process B Process C Process D Imaging E Meta-data F Gradient Boosting Model Concat Labels Prediction Deep LSTM Autoencoder Deep Convolutional Autoencoder Latent Space Representation
- 45. Predictive Quality Predictive Quality Result 1. Good performance (depending on data quality) 2. Overcomes problem of small labelled data size 3. Overcomes black-box problem ○ Explainable on process level ○ Analysis of autoencoder reconstruction errors for fine grained insights within process
- 46. Predictive Quality Predictive Quality SHAP Feature Importances
- 47. Predictive Quality Predictive Quality SHAP Single Prediction Fault 1 Fault 2 Fault 3 Prediction 0.14 0.71 0.14 SHAP Force Plot for Fault 2 SHAP Force Plot for Fault 3
- 48. Industrial AI Deep Learning for Predictive Maintenance, Predictive Quality & Visual Inspection Predictive Maintenance Visual Inspection Predictive Quality
- 49. Learnings Start simple & don’t over-engineer details Implement complete workflow asap (REST API, ...) Ensembling & combination of methods wins Keep business metrics in mind Industrial AI requires ML all-arounders
- 50. We develop prize-winning Industrial AI Simon Stiebellehner Head of AI @ craftworks Lecturer @ WU Wien & FH Wien simon.stiebellehner@craftworks.at craftworks.AI Daniel Ressi Data Scientist @ craftworks daniel.ressi@craftworks.at
- 51. Join us! craftworks.at/working-at-craftworks/ Research with us! Computer Vision Engineer / Data Scientist (Senior) Web Developer Marketeer/Growth Hacker Agile Project Manager No fit for you? Send us your application anyway - we are always looking for talent. jobs@craftworks.at Are you a student? We offer exciting research internships and thesis topics! Currently, topics are available in the areas ... Reinforcement Learning Unsupervised Deep Learning talent@craftworks.at