ADEV and friends was invented and most useful to better characterized and
compared oscillators.
To make any meaningful comparison test, the results MUST be reproducible to
some level.
Errors bands are there to show what the reproducible level is, but
unfortunately the error bands do not include temperature effects and other
systematic error causes.
You can of course use the ADEV math function for anything, but MY definition
of "true and accurate ADEV numbers" must include a set up that gives
repeatable results.
If you test is done over different or unknown temperature changes that have
major effects, then I'm calling that poor ADEV data (taking).
previously posted "The only issue I see is it may make fair comparison
difficult, unless units are compared under identical conditions".
And IMHO making a "Fair comparison" is what "true ADEV" should be for, and
when it does not do that, it becomes nothing much more than a useless number
generator.
The fact that poor ADEV answers can be used as a random number generator or
as a tool to misrepresent performance is not the fault of ADEV, but the
user.
ws
*********************
John Miles jmiles at pop.net
Indeed, ADEV is for random freq variation not easily measured by other
means.
Well, no, ADEV is the two-sample deviation of fractional frequency
differences over time. That's really all you can say about it. There's not
really any such thing as "true ADEV" -- a measurement either meets the
mathematical criteria for Allan deviation, or it doesn't.
Temperature fluctuations do not cause random freq changes and the
temperature's effect should be removed if one wants accurate long term
ADEV numbers.
No, accurate ADEV numbers are whatever you see on an accurate ADEV plot. :)
If I measure two sources in the same environment and I see HVAC ripple on
one ADEV trace but not on the other, then that may be useful information, or
even the only information I care about. (Of course, it's only useful if
the bin density is high enough to show the effect in question, but that's
not the fault of the ADEV metric itself.)
If you don't want to observe the effect of temperature fluctuations on your
DUT, random or otherwise, the correct solution is not to use a different
metric or to tweak the data, but to shield the DUT against the temperature
variations in question.
Even daily diurnal cycles due to temperature can have major negative
effect
on ADEV numbers as low as 2000 to 3000 seconds,
Your bin density may be insufficient in that case. ADEV is not unlike an
FFT in that regard -- the denser the bins, the higher the resolution,
subject to limitations imposed by the window transfer function. (Enrico
Rubiola has suggested that we should have been using FFT-like measures for
long term stability all along, instead of ADEV.)
It's true that the ADEV function is not all that sharp, but you shouldn't
ordinarily see effects removed from their causes by a 40:1 tau ratio. IMHO,
if you are seeing significant degradation at the 2000-second level caused by
diurnal cycles at the 12-hour level, something may be wrong.
Outliers are another matter, due to the infinite "ringing" that a step
function causes. They should be removed from ADEV and considered as a
separate source of error. Transients cause some pretty horrible effects in
FFTs as well, regardless of the window characteristics. Offhand, I can't
think of any simple frequency-stability metrics that are good at ignoring
outliers, and I'm not sure it'd be a good thing if we were to invent one.
and if there is an Heater or AC cycling, then any ADEV numbers about a few
hundred seconds can be due to TempCoeff, which should not be measured
with ADEV or included in ADEV plots.
Again, fractional frequency differences are fractional frequency
differences. ADEV will show temperature effects, as will an FFT or most
other metrics worth using. If you don't want to see these effects, you need
to take the appropriate measures to fix the environment, the DUT, the
instrumentation, or all of the above.
This is much the same as a single outlier data point that can screw up the
whole ADEV plot and make it pretty much meaningless and unrepeatable.
Ditto for linear ageing, Should be remove first if one wants true ADEV
plots.
Linear drift is a good thing to take out... *if* you explicitly want to
exclude it from your observation of fractional frequency-difference
statistics. Maybe you consider drift or aging to be a valid part of the
statistics you're collecting. If so, leave it in. Maybe you plan to
discipline the DUT in a way that will remove drift and aging. If so, remove
it. You're going to get a "valid" measurement of ADEV either way... but
determining whether ADEV is really the best metric to use, and interpreting
it in light of your application, is always up to you.
-- john
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