Statistical software packages allow high-level analysis of production data by connecting through historians.
One of these solutions, the R program, has become increasingly popular and is supported by Microsoft . R has been open source from the beginning and it is freely available, which has drawn a wide community to use it as their primary statistical tool. There are other reasons why it will also be very successful in manufacturing data analytics:
- R works with .NET: There are two projects that allow interoperability between R and Net called R.NET and RCLR.
- R provides a huge number of R packages (6,789 on June 18th, 2015), which are function libraries with specific focus. The package 'qcc' , for example, is an excellent library for univariate and multivariate process control.
- According to the 2015 Rexer Data Miner Survey, 76% of analytic professionals use R and 36% use it as their primary analysis tool, which makes R by far the most used analytical tool.
- Visual Studio now supports R with support for debugging and Intellisense. Visual Studio is a very popular Integrated Development Environment (IDE) for NET programmers and will make it easier for developers to start programming in R.
- R's large user base helps to review and validate packages.
- The large number of users in academia leads to the fast release of cutting edge algorithms.
Below are two examples of using R analysis in combination with the OSIsoft PI historian (+ Asset and Event Framework).
Example 1: Process Capabilities
Example 2: Principal Component Analysis of Batch Temperature Profiles
The results of the R Analysis can also be used in real time for process analysis. In general, the process of model development and deployment is structured as follows:
In the model development phase, models such as SPC, MPC, PCA or PLS are developed, validated and finally stored in a data file. During the real time application or model deployment phase, new data are sent to R and the same model is used for prediction.
There is an increasing gap in manufacturing between the amount of data stored and the level of analysis being performed. The R statistical software package can close that gap by providing high level analysis of production data that are provided by historians such as OSIsoft PI. It provides a rich library of statistical packages that perform univariate and multivariate analysis and allows real time analytics.
This post was written by Dr. Holger Amort. Holger is a senior consultant at Maverick Technologies, a leading automation solutions provider offering industrial automation, strategic manufacturing, and enterprise integration services for the process industries. Maverick delivers expertise and consulting in a wide variety of areas including industrial automation controls, distributed control systems, manufacturing execution systems, operational strategy, business process optimization and more.