Tom, Magnus, Ulrich: Thanks for the comments and suggestions. They are appreciated and I now have an even better understanding of why ADEV measurements are not a good tool for characterizing the performance of oscillators that are subject to transient events or glitches.
Just to clarify a few points and ask a few questions: My concern about not putting much emphasis on Adev data for Tau's of less than 80 seconds in the plots I’ve provided is driven by a belief that at shorter Tau's these ADEV plots are largely showing the noise of the counter (an HP5370B) vs the noise of the device being measured. Perhaps the 80 second cut off point is overly conservative but at some point I believe the counter noise will swamp the noise from the devices being measured. My goal was not to try and use ADEV measurements to characterize the performance of the GPSDO in question while it was subject to fluctuations in air flow (or subject to other transient events..) I did include a frequency plot in my post that provides some insight as to what happened when air flow was added. The goal was to see if operating the GPSDO in question with air flow changed the ADEV readings vs operating the GPSDO without air flow. I agree ADEV may not be the best tool for this but it is easy to collect and I have prior data to compare the results to. ADEV also seems to be a commonly used figure of merit for characterizing devices such as GPSDO’s. (I realize there are also other commonly used figures of merit.) The lowest ADEV reading I have ever observed for the GPSDO in question came from analyzing a data set collected 45 thru 65 minutes after air flow was applied to that GPSDO in that particular circumstance. I found that result surprising although I agree the absolute difference in the ADEV figures is very small. It's my understanding (based largely on comments I've read on this list over the years) that if you have roughly nx10 data points you can begin to draw inferences from ADEV plots for Taus <n. Is this a reasonable practice and or are there caveats one needs to be aware of ? I agree that one test of this nature is in sufficient to draw any firm conclusions from and much more data is needed. Regards Mark Spencer > Message: 5 > Date: Tue, 25 Dec 2012 03:12:51 +0100 > From: Magnus Danielson <mag...@rubidium.dyndns.org> > To: time-nuts@febo.com > Subject: Re: [time-nuts] Z3805A cooling requirements? > Message-ID: <50d90ba3.5060...@rubidium.dyndns.org> > Content-Type: text/plain; charset=ISO-8859-1; format=flowed > > Hi, > > On 12/24/2012 06:47 PM, Tom Van Baak wrote: > > Hi Mark, > > > >> I wouldn't place much emphasis on the Adev data for > Tau's of less than 80 seconds. > > > > Actually, just the opposite; the ADEV at short tau is > very close to correct. > > > >> I've collected some ADEV data as well but don't > entirely trust it yet. > > > > Right, it's the ADEV for longer tau that is completely > misleading. Let me explain. > > > > Realize that Allan deviation numbers are statistics; > essentially they predict > > how constant the future frequency might be, based on a > sampling of measured > > frequency in the past. For a statistic like this to be > relevant you want to > > have at least 3, but more likely tens to hundreds of > past measurements in > > order to have confidence in the prediction. > > Here I want to point out what this is the statistics off, > and it is the > statistics noise, normalized to white frequency noise. It is > however not > statistics of systematic effects. > > > Plotting many Allan deviation statistics, each with a > different sampling > > interval, on a log-log plot gives even more > information; the slope of the > > line reveals noise types. > > Which is the original intent of the ADEV/MDEV curves. MDEV > is being > preferred as it helps to distinguish two noise forms that > ADEV failed to > handle. For far-out noises, ADEV does just was well with > less processing > needed. > > > Now, there is no problem observing transient phenomenon > like temperature > > changes (or phase jumps or frequency jumps or loose > cables or pets > > jumping onto the bench). They show up dramatically in > phase or frequency > > strip plots. You can see how quickly the effect occurs. > You can measure > > the magnitude of the effect. You can measure how long > it takes to recover. > > This is all useful: you get numbers like tempco or > thermal Q. But using > > standard deviation or RMS or Allan deviation, or any > other *statistic* > > on this data is not the right thing to do -- because > you have only a sample of one. > > Consider if you wrap the time-sequence to re-occur at the > same period. > If this is the signal you have, then it is valid. If you > include more > "un-eventfull" time and wrap that, then this wrinkle has > less part of > the overall time, and thus is averaged out. Assume you keep > extending > with un-eventfull time to double each time you end up > averaging the > particular wrinkle out of the plot, but still only approach > an > approximation as a single wrinkle only occur once. > > Those, the ADEV tool-set isn't going to give you very > meaningful > interpretation of that wrinkle. > > > On the other hand, if you encounter tens or hundreds of > these > > transients over hours or days or months, then it is > perfectly > > valid to use statistics like standard or Allan > deviation to > > describe the probability of the transient occurring; > the > > magnitude of the effect, etc. Now you have enough > events to > > offer a future prediction based on many samples in the > past. > > Here I don't agree. Re-occuring "wrinkles" is systematic > effects, and > the impact of systematic effects is different to those of > noise forms. > > A sine modulation of frequency and the way we can estimate > it's impact > on future time is quite different from that of inherent > noise sources. > Also, it doesn't scale to the white frequency noise. > > Similarly, other systematic effects should be separated out > of the data > before noise analysis. > > > Does this make sense? In your case "removing air flow" > is only one event. > > Indeed. In my experience, forced air as such does not need > to be the > culprit, it just optimize the coupling between ambient > temperature > changes and the oscillator. Varying forced air rate also > counts as > inducing temperature gradients. > > > I know it's easy to make ADEV/MDEV plots using Plotter > or Timelab > > but that doesn't mean it's appropriate in every case. > When your > > data has an accidental data glitch or an intentional > transient, > > it's best not to use statistics to describe that one > event. > > In fact, looking at SP 1065 for instance, cleaning your data > of such > events is assumed normal procedure. > > Cheers, > Magnus > > > > ------------------------------ > > _______________________________________________ > time-nuts mailing list > time-nuts@febo.com > https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts > > End of time-nuts Digest, Vol 101, Issue 169 > ******************************************* > _______________________________________________ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
