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SECTION 11. OUTPUT PROCESSING INSTRUCTIONS
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
o'
=
:ll3'o?',:'f,
f
i"(Pi.:i'o
*
Storage)
03
= Input Storage Area
Q2: 4
Starting input location destination
if
option 03
Output
Array lD
if
options 0-2
(1-511 are
valid lDs)
*** 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
is
50/5
=
10.
Cycles with an amplitude,
A, less
than
10
willbe 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
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 cyc
which have
a mean value, M,
100
<
M
<
110,
second bin 110 <
M
<
120.
etc.
lf Cma
is
the count
for mean range m and
amplitude range a, and M and N are
the
of mean and amplitude bins respectively; then
the output of
one repetition
is
arranged
sequentially
as
(C1,1,
C1,2,
...
C1,N,
C2,1,
C2,2,
..
Cr'r.ru).
Multiple repetitions are sequential
in
memory. Shown
in
two dimensions,
the output
is:
cr,r
Qz,t
c.r,z
cz.z
ct,r.t
cz,tt
Ct',1,t
Cu,z
cu,ru
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.
11-6
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