Statistical computing in manufacturing through historians

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 [1][2][3][4]. 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:

  1. R works with .NET: There are two projects that allow interoperability between R and Net called R.NET[5] and RCLR[6].
  2. R provides a huge number of R packages (6,789 on June 18th, 2015), which are function libraries with specific focus. The package ‘qcc’ [7], for example, is an excellent library for univariate and multivariate process control.
  3. According to the 2015 Rexer[8] 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.
  4. 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.
  5. R’s large user base helps to review and validate packages.
  6. 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 

Figure 1: Process capability of QC parameter. Data loaded using RCLR+OSIsoft Asset Framework SDK. Analysis shows the lower control limit will lead to a rejection rate of 0.5% (CpL < 1.0). Courtesy: Maverick Technologies

Example 2: Principal Component Analysis of Batch Temperature Profiles 

Figure 2: PCA Biplot. Data and Batch Event Frames were loaded using RCLR+OSIsoft Asset Framework SDK. Courtesy: Maverick Technologies

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:

Figure 3: Model development and deployment using R. Models are developed in R and results are written back to the historian in real-time. Courtesy: Maverick Technologies

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. 

Figure 4: Single and Trend Process Control Limits example. Control Limits are fed back to the historian ??? The dotted vertical line represents the current time. Courtesy: Maverick Technologies

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.












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