Promoting the use of R in the NHS

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(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
We are using R, more information about R:

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