Hi Scott.

Here is a textfile with data for the 10 years (As in the graph 2001-2011).

Also the ln(bt+1) fit, as Magnus said, has the derivate b/(b*t+1) that with b*t 
>>1 is 1/t. But my data has the aging between 1 and 10 years more like 
1/sqrt(t) If I just have a brief look on the aging graph.

Lars

Från: Scott Stobbe<mailto:[email protected]>
Skickat: den 19 november 2016 04:11

Hi Lars,

I agree with you, that if there is data out there, it isn't easy to find,
many thanks for sharing!

Fitting to the full model had limited improvements, the b coefficient was
quite large making it essentially equal to the ln(x) function you fitted in
excel. It is attached as "Lars_FitToMil55310.png".

So on further thought, the B term can't model a device aging even faster
than it should shortly after infancy. In the two extreme cases either B is
large and (Bt)>>1 so the be B term ends up just being an additive bias, or
B is small, and ln(x) is linearized (or slowed down) during the first bit
of time.

You can approximated the MIL 55310 between two points in time as

f(t2) - f(t1) = Aln(t2/t1)

A = ( f(t2) - f(t1) )/ln(t2/t1)

Looking at some of your plots it looks like between the end of year 1 and
year 10 you age from 20 ppb to 65 ppb,

A ~ 20

The next plot "Lars_ForceAcoef", is a fit with the A coefficient forced to
be 2 and 20. The 20 doesn't end-up fitting well on this time scale.

Looking at the data a little more, I wondered if the first 10 day are going
through some behavior that isn't representative of long-term aging, like
warm-up, retrace (I'm sure bob could name half a dozen more examples). So
the next two plots are fits of the 4 data points after day10, and seem to
fit well, "Lars_FitAfterDay10.png", "Lars_1Year.png".

If you are willing to share the next month, we can add that to the fit.

Cheers,

On Fri, Nov 18, 2016 at 1:26 PM, Lars Walenius <[email protected]>
wrote:
>
> Hopefully someone can find the correct a and b for a*ln(bt+1) with
stable32 or matlab for this data set:
> Days ppb
> 2       2
> 4       3.5
> 7       4.65
> 8       5.05
> 9       5.22
> 12     6.11
> 13     6.19
> 25     7.26
> 32     7.92

days    ppb
2       2
4       3.5
7       4.65
8       5.05
9       5.22
12      6.11
13      6.19
25      7.26
32      7.92
33      8.15
39      8.42
46      8.92
46      9.18
47      9.02
54      9.51
60      9.78
74      10.45
83      11.36
92      11.78
97      12.08
110     12.8
128     13.6
158     14.7
193     15.9
224     16.9
254     18
284     19.01
314     20
343     21.1
375     22.3
417     23.7
445     24.8
476     25.7
515     26.7
545     27.7
586     28.8
615     29.4
646     30.2
672     30.7
703     31.4
743     32.2
787     33.5
826     34.6
861     35.3
904     36.2
940     37
976     37.7
1010    38.5
1046    39.1
1072    39.5
1081    38.3
1124    38.6
1163    39.6
1201    40.5
1239    41.2
1285    42.2
1320    43
1357    43.6
1398    44.4
1432    45
1467    45.7
1501    46.3
1534    47.1
1560    47.7
1597    48.4
1628    48.9
1659    49.3
1687    49.9
1718    50.35
1748    50.7
1779    51.2
1809    51.5
1850    52
1886    52.6
1914    53
1947    53.45
1984    53.9
2019    54.3
2045    54.5
2071    54.7
2116    55.11
2132    54.97
2165    55.27
2198    55.63
2230    56
2265    56.5
2309    57
2346    57.4
2389    57.85
2434    58.35
2473    58.7
2513    59
2550    59.3
2587    59.65
2625    60
2664    60.35
2696    60.6
2729    60.85
2764    61.15
2796    61.4
2829    61.6
2863    61.85
2898    62.1
2935    62.4
2972    62.8
3007    63.12
3043    63.47
3078    63.73
3114    64.05
3149    64.3
3184    64.5
3224    64.75
3259    64.96
3295    65.23
3332    65.54
3368    65.82
3406    66.09
3441    66.33
3482    66.55
3519    66.72
3563    67.01
3604    67.22
3647    67.59
3692    68.02
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