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Applying a Prediction or regression model based on one set of signals to another set of signals


Sean T

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Scenario: I have created a regression or prediction model for my process but i want to apply that same regression model to another set of signals or a different period of time.

This could be helpful for comparing how one piece of equipment is operating when compared against a regression built for another system. It could be used to predict how a system will behave based on how some other similar system behaved. It could be used predict how a system will behave before you have enough information to build that system or run its own model, or any number of applications where we might want to apply a regression or prediction model built on one set of signals to another set of signals.

Solution:

First off, we have to build a prediction model! In this case i have modeled a Filtration system, predicting filter head loss. I took into account the time the filter has been in service, the raw water turbidity, and the Filter turbidity. 

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Next, if i click on the information icon for my prediction signal, i am able to see the formula that was used to create this prediction. We will need to copy this formula: 

image.png

Next, I need to identify my new signals. In my case, i am going to apply this prediction model to Filter 12. Once i have all of my signals that will be sued to the prediction i can paste my formula into a new formula and make sure all my variables line up (or, alternately and a bit easier, just use the duplicate to formula function from the information screen shown above by clicking on the down arrow next to the duplicate button).

Finally, I need to update the PREDICT section of my formula denoted by the .predict() function with the signals i will apply my prediction model to. I can do this by using the search button in the formula tool to add my new signals, and then update the .predict() section of my formula with my new variables, make sure to put them in correctly and in the right order otherwise your model will be off!

image.png

 

Finally, i can use my prediction model that i built for Filter 1 on Filter 12 and derive further insight, in this case i might question whether my systems are truly as similar as i think they are or whether there is something causing my model to deviate from what was expected for filter 12. In my case, both prediction models are in red:

image.png

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