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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.orIsolation 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 clo…
Recent posts

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

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.
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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 c:/tmp

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 the last 24 hours of testing station data.
Note. On a real environment, get and process testing stations data one by one …

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

Introduction:

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: 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

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 …

Displaying our "R - Quality Control Individual Range Chart Made Nice" inside a Java web App using AJAX - How To.

Prerequisites:What you should have installed:Java, it can be OpenJDK, you can get it from here: https://github.com/ojdkbuild/ojdkbuildTomcat, any version from 8 up.Eclipse EE: Eclipse IDE for Java EE.Spring Tools Suite For Eclipse: https://spring.io/tools. You can install it from Eclipse Marketplace.R: https://www.r-project.org. Inside R, at least these packages:install.packages( "tidyverse", dependencies = TRUE )install.packages( "rmarkdown", dependencies = TRUE )install.packages( "dygraphs", dependencies = TRUE )install.packages( "qcc", dependencies = TRUE )install.packages( "rattle", dependencies = TRUE )install.packages( "Rcmdr", dependencies = TRUE )install.packages( "stlplus", dependencies = TRUE )Rstudio, to edit your RMarkdown files: https://www.rstudio.com.Rtools: https://cran.r-project.org/bin/windows/Rtools/index.html.Pandoc, this is to convert markdown files to html: https://pandoc.org.Fork git utility:

R - Quality Control Individual Range Chart Made Nice.

In R we have the qcc package but charts are not very nice, specially if you want to put your chart in a HTML file.

Here I describe the process of creating the chart starting by using the qcc package and ending by using our own calculations and a nice dygraphs chart.

You might avoid all the comments if you go directly to my github.com repository:

https://github.com/LaranIkal/R-ANALYTICS

Note. Due to github restrictions for html files sizes, the html file needs to be downloaded before you can open it.

If you want to continue here, you can see the R code and outputs I copied from the html file( QualityControl_IndividualRangeChart.html ) result from the R markdown file( QualityControl_IndividualRangeChart.Rmd ) on my github.com repository:

# Loading needed libraries# R quality control library suppressWarnings( suppressMessages( library( qcc ) ) ) # One of the R nice charts library suppressWarnings( suppressMessages( library( dygraphs ) ) ) measurements = c( -0.001, -0.011, .2, 0.001, -0.01…