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Resistance as a function of temperature

andreacoombs

I have a customer who would like to reduce the number of measurements needed to calibrate temperature probes that they make. The have lots of historical data and they have a process that results in variable curves for resistance as a function of temperature. I am wondering if anyone has experience with this type of problem and how JMP can help. Thanks, Andrea

Accepted answers

Jason_Wiggins

Hey Andrea,

The Nonlinear DOE will identify how many and where measurements should be taken to get accurate model parameter estimates and predictions. For resistance vs. temperature, I expect that the recommended measurements will be more frequent and closer together in non-linear or curved regions where measurements will be more sparse and spread in linear regions. The measurement plan you get from Nonlinear DOE will likely require fewer measurements than they are doing now.

All comments

Mark_Bailey

What is their current calibration method or process? What does a calibration curve look like? Do they currently have a parametric model for the curve? Do they have calibration problems besides excess testing? Can they provide a sample of curves?

andreacoombs

All we know so far is that they have tried lots of models that haven't worked. We are talking with them again on Wednesday after our NDA is in place and they are going to show us curves and what they've tried. I can provide more information then.

Jason_Wiggins

Hey Andrea,

Resistance as a function of temperature is a non-linear relationship.It is highest at lower temps and is low at higher temps. There are two approaches I have used to model data like this...

Start with a theoretical model from the literature for the material of interest. You can fit custom non-linear models in the nonlinear regression platform. The procedure for doing this is in the help documentation.

https://www.jmp.com/support/help/en/15.1/#page/jmp/nonlinear-regression.shtml#

The other approach is when a model from theory is not available. In this case use the fit curve platform to explore candidate models. If one is found then save the parametric prediction formula to the data table then use the formula in the non-linear platform to fit other candidate curves.

Either method you want a predictor formula. You can then use the predictor formula as X and response from one of the curves in DOE > Special Purpose > Nonlinear Design to come up a plan that requires fewer measurements to fit their curves.

https://www.jmp.com/support/help/en/15.1/?os=win&source=application#page/jmp/augment-a-design-using-prior-data.shtml#ww89885

Happy to talk it over with you if it seems like either approach could be helpful.

Enjoy!

Mark_Bailey

FDE might offer some advantages of JMP Pro for this application as well.

Peter_Polito

When working with resistance temperature readings in the past I have used the Steinhart-Hart equation to calibrate thermistors (you mentioned resistance sensors, not sure if they are using thermistors). The Steinhart-Hart equation allows the user to develop a calibration curve with only a few readings using a three coefficients (A, B, and C) derived from 3 or more readings. Perhaps they can use this method, using 3 (or more) measurements to develop the calibration curve and current or historical data to estimate uncertainty. This might be a more advantageous route for them if they are using thermistors (though given the min temp of 0 K, perhaps not). https://en.wikipedia.org/wiki/Steinhart%E2%80%93Hart_equation