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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

We are using R, more information about R:


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Using R and H2O to identify product anomalies during the manufacturing process.


We will identify anomalous products on the production line by using measurements from testing stations and deep learning models. Anomalous products are not failures, these anomalies are products close to the measurement limits, so we can display warnings before the process starts to make failed products and in this way the stations get maintenance.

 Before starting we need the next software installed and working:

- R language installed.
- All the R packages mentioned in the R sources.
- Testing stations data, I suggest to go station by station.
- H2O open source framework.
- Java 8 ( For H2O ). Open JDK:
- R studio.

Get your data.
About the data: Since I cannot use my real data, for this article I am using SECOM Data Set from UCI Machine Learning Repository

How many records?:  Training data set - In my real project, I use 100 thousand test passed records, it is around a month of production data. Testing data set - I use …

Predicting Car Battery Failure With R And H2O - Study

Using R and H2O Isolation Forest to predict car battery failures. Carlos Kassab 2019-May-24
This is a study about what might be if car makers start using machine learning in our cars to predict falures.

# Loading libraries suppressWarnings( suppressMessages( library( h2o ) ) ) suppressWarnings( suppressMessages( library( data.table ) ) ) suppressWarnings( suppressMessages( library( plotly ) ) ) suppressWarnings( suppressMessages( library( DT ) ) ) # Reading data file# Data from: dataFileName = "/Development/Analytics/AnomalyDetection/AutomovileFailurePrediction/v2.csv" carData = fread( dataFileName, skip=0, header = TRUE ) carBatteryData = data.table( TimeStamp = carData$timeStamp , BatteryVoltage = as.numeric( carData$battery ) ) rm(carData) # Data cleaning, filtering and conversion carBatteryData = na.omit( carBatteryData ) # Keeping just valid Values# Acco…