Post on 18-Mar-2021
M2L® Anomaly Detection
Benefits • Identify failures that are missed by QC • Identify glitch in service network • User-friendly • Plug & Play
Use case: towards zero defects in glass production
Customisation
• Anomaly detection model that links the occurrence of blisters to outliers in specific sensor readings 12-24 hours earlier in the process.
Business case
• Detecting production settings leading to too many blisters, thus reducing the amount of glass that has to be discarded if acted upon in time.
Radboud University Spin-offinfo@machine2learn.com
Exclusive to M2L® • Root cause determination • Combining academic and industrial
experience • High precision via customisation • Deployable on edge devices • Integration with cloud
Examples anomalous temperature sensors, defective land-line connection
Challenge
• Subtle changes in the glass production process lead to blisters in the final product
• Batches of glass with too many blisters have to be discarded
• Large but incomplete database of historic sensor readings from a glass furnace