Production Line Stations Maintenance Prediction – Process Flow.

May 14, 2019

(This article was first published on R-Analytics, and kindly contributed to R-bloggers)

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

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