Operator`s manual

SECTION 11. OUTPUT PROCESSING INSTRUCTIONS
11-6
The output flag must be set each time
Instruction 80 is used. Instruction 80 must
directly follow the instruction that sets the output
flag.
PAR. DATA
NO. TYPE DESCRIPTION
01: 2 Storage area option
01 = Final Storage (00 and 02
also default to Final
Storage)
03 = Input Storage Area
02: 4 Starting input location destination
if option 03
Output Array ID if options 0-2
(1-511 are valid IDs)
*** 81 RAINFLOW HISTOGRAM ***
The Rainflow Instruction implements the rainflow
counting algorithm, essential to estimating
cumulative damage fatigue to components
undergoing stress/strain cycles. Data can be
provided by making measurements in either the
standard or the burst mode. The Rainflow
Instruction can process either a swath of data
following the burst mode, or it can process "on
line" similar to other processing instructions.
The output is a two dimensional Rainflow
Histogram for each sensor or repetition. One
dimension is the amplitude of the closed loop
cycle (i.e., the distance between peak and
valley); the other dimension is the mean of the
cycle (i.e., [peak value + valley value]/2). The
value of each element (bin) of the histogram can
be either the actual number of closed loop
cycles that had the amplitude and average value
associated with that bin or the fraction of the
total number of cycles counted that were
associated with that bin (i.e., number of cycles in
bin divided by total number of cycles counted).
The user enters the number of mean bins, the
number of amplitude bins, and the upper and
lower limits of the input data.
The values for the amplitude bins are
determined by difference between the upper and
lower limits on the input data and by the number
of bins. For example, if the lower limit is 100
and the upper limit is 150, and there are 5
amplitude bins, the maximum amplitude is 150 -
100 = 50. The amplitude change between bins
and the upper limit of the smallest amplitude bin
is 50/5 = 10. Cycles with an amplitude, A, less
than 10 will be counted in the first bin. The
second bin is for
10 ≤ A < 20, the third for 20 ≤ A < 30, etc.
In determining the ranges for mean bins, the
actual values of the limits as well as their
difference are important. The lower limit of the
input data is also the lower limit of the first mean
bin. Assume once again that the lower limit is
100, the upper limit 150, and that there are 5
mean bins. In this case the first bin is for cycles
which have a mean value, M, 100 ≤ M < 110, the
second bin 110 ≤ M < 120, etc.
If Cma is the count for mean range m and
amplitude range a, and M and N are the number
of mean and amplitude bins respectively; then
the output of one repetition is arranged
sequentially as (C
1,1
, C
1,2
, ... C
1,N
, C
2,1
, C
2,2
, ...
C
M,N
). Multiple repetitions are sequential in
memory. Shown in two dimensions, the output
is:
C
1,1
C
1,2
... C
1,N
C
2,1
C
2,2
... C
2,N
... .
.. ..
.. ..
C
M,1
C
M,2
... C
M,N
The histogram can have either open or closed
form. In the open form, a cycle that has an
amplitude larger than the maximum bin is
counted in the maximum bin; a cycle that has a
mean value less than the lower limit or greater
than the upper limit is counted in the minimum
or maximum mean bin. In the closed form, a
cycle that is beyond the amplitude or mean
limits is not counted.
The minimum distance between peak and
valley, Parameter 8, determines the smallest
amplitude cycle that will be counted. The
distance should be less than the amplitude bin
width ([high limit - low limit] / no. amplitude bins)
or cycles with the amplitude of the first bin will
not be counted. However, if the value is too
small, processing time will be consumed
counting "cycles" which are in reality just noise.