File:MATLABUChart.png

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Description
English: A en:MATLAB-generated en:u-chart for a process that experienced a 1.5σ drift starting at midnight.
Date
Source Own work
Author DanielPenfield

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I, the copyright holder of this work, hereby publish it under the following license:
w:en:Creative Commons
attribution share alike
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You are free:
  • to share – to copy, distribute and transmit the work
  • to remix – to adapt the work
Under the following conditions:
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  • share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.

Source code[edit]

en:Perl[edit]

#!/usr/bin/perl -w

#
# randomly generate process observations that simulate a
# Poisson-distributed process in the state of statistical control
# (c_setup.csv) and simulate the same process experiencing a drift of
# magnitude $drift starting two hours into the $shift shift
# (c_monitoring.csv)
#

use strict;
use Math::Random;

my %shiftSchedule = (
    "first" =>  { "start" =>  6.00, "end" => 14.00 },
    "second" => { "start" => 14.00, "end" => 22.00 },
    "third" =>  { "start" => 22.00, "end" =>  6.00 }
);
my $shift = "third";         # shift to monitor
my $inspectionRate = 1 / 2;  # every 1/2 hour
my $drift = 1.5;             # sigma drift to simulate
my $m = 25;                  # samples in control chart setup
my $target = 0.10;           # fraction nonconforming target

my $c = $target;
my $n = 48;                  # observations per sample
my $hour;
my $i;
my $minute;
my @observations;
my $observation;
my $setupM = $m;

#
# simulate control chart setup
#
open(SETUPCSV, ">c_setup.csv") || die "! can't open \"c_setup.csv\" ($!)\n";
for ($i = 1; $i <= $m; $i++) {
    @observations = Math::Random::random_poisson($n, $c);
    $observation = 0;
    map { $observation += $_ } @observations;
    print SETUPCSV $observation . "\r\n";
}
close(SETUPCSV);

#
# simulate control chart monitoring
#
open(MONITORINGCSV, ">c_monitoring.csv") || die "! can't open \"c_monitoring.csv\" ($!)\n";
$m = $shiftSchedule{$shift}{"end"} - $shiftSchedule{$shift}{"start"};
if ($m < 0) {
    $m += 24;
}
$m /= $inspectionRate;
for ($i = 1; $i <= $m; $i++) {
    $hour = int($i * $inspectionRate + $shiftSchedule{$shift}{"start"});
    if ($hour >= 24) {
        $hour -= 24;
    }
    $minute = ($i & 0x1) ? (60 * $inspectionRate) : 0;
    if ($i >= (0.25 * $m)) {
        if ($i < (0.75 * $m)) {
            $c = $target + ($drift * $target / (0.5 * $m)) * ($i - (0.25 * $m));
        } else {
            $c = $target + $drift * $target;
        }
    }
    @observations = Math::Random::random_poisson($n, $c);
    $observation = 0;
    map { $observation += $_ } @observations;
    printf MONITORINGCSV "'%d:%02d',%d\r\n", $hour, $minute, $observation;
}
close(MONITORINGCSV);

en:MATLAB[edit]

%
% display a u-chart control chart in MATLAB
%
clear
%
% rational subgroup size
%
n = 48

%
% Phase I
%
% compute the control chart center line and control limits based on a
% process that is simulated to be in a state of statistical control
%
% note that MATLAB requires full counts for both c- and u-charts (as
% opposed to requiring the user to precalculate the average number of
% nonconformities per rational subgroup)
%
setupobservations = csvread('c_setup.csv');
setupstats = controlchart(setupobservations, 'charttype', 'u', 'unit', n);

%
% Phase II
%
% read in the process observations representing the monitoring phase
%
observations = importdata('c_monitoring.csv');

%
% first column is the time of the observation (24 hour clock)
%
halfhourlylabel = observations.rowheaders;
%
% second column consists of the observations (counts of
% nonconformances per rational subgroup)
%
monitoringobservations = observations.data;

%
% just display labels on the "on the hour" ticks
%
emptylabel = cell(size(monitoringobservations,1) - size(halfhourlylabel,1), 1);
emptylabel(:) = {''};
hourlylabel = vertcat(halfhourlylabel(2:2:end), emptylabel);

%
% plot the control chart for the monitoring phase observations based
% on the "in control" estimates for the process mean
%
monitoringstats = controlchart(monitoringobservations, ...
							   'charttype', 'u', ...
							   'label', halfhourlylabel, ...
							   'unit', n, ...
							   'mean', setupstats.m, ...
							   'sigma', setupstats.m);
title('u chart for quality characteristic XXX')
xlabel('Sample')
ylabel('Nonconformances per unit')
%
% the labels supplied to controlchart() only appear when the user
% selects a plotted point with her mouse--we have to explicitly
% set labels in the X axis if we want them
%
set(gca,'XTickLabel', hourlylabel)

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current14:05, 22 June 2013Thumbnail for version as of 14:05, 22 June 2013560 × 420 (4 KB)DanielPenfield (talk | contribs)User created page with UploadWizard

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