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Steps Needed in a Process to Detect Anomalies And Have a Maintenance Notice Before We Have Scrap Created on The Production Line.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
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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