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![]() Measurement, Test and Calibration
![]() Thinking of Measurement as a System
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Marc Smith Cheech Wizard Posts: 4119 |
I ran across this in the ISL listserve. I am posting it because IMHO it shows a good thought train in a calibration system. QS9000 auditors are more and more asking for details of how a company complies with MSA. Some oldies are being discredited (you do *NOT* have to do R&R on every Control Plan line item) and many new concepts are being asked about (addressing linearity, bias, stability, etc.). They are looking to see logic and understanding throughout the process from choosing M&TE during the design stage (automotive=APQP) through and in production. This has to do with 'test' equipment, but the concepts within are good thought provokers. Oh well, FYI - I think it is a good read: -------snippo------- >From: "SMITH, RON D. (JSC-EM)" > >> I am having to prepare a process for handling the calibration of IMTE that
>> >Ron Smith >Johnson Space Center >Measurement Standards and Calibration Laboratory >Houston, Texas
From: [email protected] (Doug Pfrang) Subject: Re: Q: Long Term Testing/Smith/Pfrang
Ron,
The fact that there is no way to remove the measurement equipment once testing has started is not especially relevant to your problem. Calibration is always something that is performed at a single moment in time and then is ASSUMED to be valid until the next moment in time when another calibration is performed. ANY piece of IMTE can go out of calibration in the interim. Therefore, it doesn't really matter that there is no way to remove the instruments once testing has started; regardless of whether you can remove it or not (i.e., to check the calibration), the real issue is how inherently STABLE your IMTE is over whatever interim you're using between calibrations.
The answer to this question depends entirely on your SELECTION of IMTE. If you know that you must go X years between calibrations, then you have to SELECT equipment that is going to be stable over that period. Once the equipment is selected and installed, there's not much you can do beyond what you are already doing -- namely, monitoring the output to see if there are any unusual changes in the data. If there are unusual changes in the data, then the question is: are the changes real (i.e., have you detected a real change in what you're testing), or are they due to a malfunction in your measurement equipment.
To answer this question, you presumably, at some point, can take the experiment apart and recalibrate your measurement equipment so see if it is still in calibration. If it is not in calibration, then you generally assume that it drifted out of calibration sometime during your experiment, and your data becomes suspect. You might be able to salvage your data if you can determine when the drift occurred and by how much. You can then go back and correct the data post hoc, compensating for the errors that were introduced by your test equipment.
But even if your measurement equipment is still in calibration at the end of your experiment, and you assume that it stayed in calibration throughout the experiment, you are still making a leap of faith, because you are calibrating the equipment only at isolated moments in time and then ASSUMING the equipment did not change significantly in the interim. This is not necessarily a valid assumption. Therefore, you should still do a statistical evaluation of your data (both during and after the experiment), and of your test equipment (after the experiment), to check for variability and trends, because you can never be absolutely positive that the data you collected are free from malfunctions in your measurement equipment. All you can do is increase the LIKELIHOOD that the data are free from equipment error.
Bottom line: Don't worry that you can't recalibrate the equipment without disrupting the experiment, just continue the experiment and continue examining the data during the experiment. Then, at the end of the experiment, revalidate the test equipment, analyze the data thoroughly to look for ALL possible sources of experimental error (including those caused by faulty test equipment), and draw the best conclusions you can with what you have.
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