R-Analytics

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: https://www.kaggle.com/yunlevin/levin-vehicle-telematics 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 ) # Kee

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

Note.  - This article  has some improvements from  Yana Kane-Esrig(  https://www.linkedin.com/in/ykaneesrig/  ) , mentioned in this article:  http://laranikalranalytics.blogspot.com/2021/03/updated-using-r-and-h2o-to-identify.html Introduction: We will identify anomalous units on the production line by using measurements data from testing stations and Isolation Forest model. Anomalous products are not failures, anomalies are units close to measurement limits, so we can display maintenance warnings before the station starts to make scrap. Before starting we need the next software installed and working: -  R language installed. -  H2O open source framework. - Java 8 ( For H2O ). Open JDK:  https://github.com/ojdkbuild/contrib_jdk8u-ci/releases -  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      I downloaded SECOM data to /tmp How many records?:  Traini