Top Banner
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-off [email protected] 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
1

M2L Anomaly Detection - machine2learn.com · M2L® Anomaly Detection Benefits • Identify failures that are missed by QC • Identify glitch in service network • User-friendly

Mar 18, 2021

Download

Documents

dariahiddleston
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: M2L Anomaly Detection - machine2learn.com · M2L® Anomaly Detection Benefits • Identify failures that are missed by QC • Identify glitch in service network • User-friendly

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 [email protected]

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