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