Production Line Stations Maintenance Prediction - Process Flow.

Steps Needed in a Process to Detect Anomalies And Have a Maintenance Notice Before We Have Scrap Created on The Production Line.

Describing my previous articles( 1, 2 ) process flow:

• Get Training Data.
• At least 2 weeks of passed units measurements.

• Data Cleaning.
• Ensure no null values.
• At least 95% data must have measurement values.

• Anomalies Detection Model Creation.
• Deep Learning Autoencoders.
• or
• Isolation Forest.

• Set Yield Threshold Desired, Normally 99%

• Get Prediction Value Limit by Linking Yield Threshold to Training Data Using The Anomaly Detection Model Created.

• Get Testing Data.
• Last 24 Hour Data From Station Measurements, Passed And Failed Units.

• Testing Data Cleaning.
• Ensure no null values.

• Get Anomalies From Testing Data by Using The Model Created And Prediction Limit Found Before.

• If Anomalies Found, Notify Maintenance to Avoid Scrap.

• Display Chart Showing Last 24 Hour Anomalies And Failures Found:

As you can see( Anomalies in blue, Failures in orange ), we are detecting anomalies( Units close to measurement limits ) before failures.

Sending an alert when the first or second anomaly was detected will prevent scrap because the station will get maintenance to avoid failures.

Carlos Kassab

https://www.linkedin.com/in/carlos-kassab-48b40743/

We are using R, more information about R:

https://www.r-bloggers.com