
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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