We create this package, mvMonitoring, from the
foundation laid by Kazor et al (2016). This package is designed to make
simulation of multi-state multivariate process monitoring statistics
easy and straightforward, as well as streamlining the online process
monitoring component.
As of 18 October, 2017, we have submitted this package to CRAN. Shortly thereafter, you will be able to install the stable version of the package via
install.packages("mvMonitoring")Make sure you have the latest version of the devtools
package, and pull the package from GitHub. Remember, if you can read
this file, you are one of the few people who have access to this
package. In addition, you can also run the following:
devtools::install_github("gabrielodom/mvMonitoring", auth_token = "tokenHere")where you create the value of “tokenHere” by generating a personal access token (PAT) at https://github.com/settings/tokens and copying the quoted string to this argument. Load the library after installation by
library(mvMonitoring)These are the examples shown in the help files for the mspProcessData(), mspTrain(), mspMonitor(), and mspWarning() functions.
# Generate one week's worth of normal operating (NOC) data recorded at the one-
# minute level
nrml <- mspProcessData(faults = "NOC")
# The state values are recorded in the first column.
n <- nrow(nrml)
# Calculate the training summary, but save five observations for monitoring.
# This function will treat the first 3 days as in control (IC), and then update
# the training window each day.
trainResults_ls <- mspTrain(
data = nrml[1:(n - 5), -1],
labelVector = nrml[1:(n - 5), 1],
trainObs = 4320
)
# While training, we included 1 lag (the default), so we will also lag the
# observations we will test.
testObs <- nrml[(n - 6):n, -1]
testObs <- xts:::lag.xts(testObs, 0:1)
testObs <- testObs[-1,]
testObs <- cbind(nrml[(n - 5):n, 1], testObs)
# Run the monitoring function.
dataAndFlags <- mspMonitor(
observations = testObs[, -1],
labelVector = testObs[, 1],
trainingSummary = trainResults_ls$TrainingSpecs
)
# Alarm check the last row of the matrix returned by the mspMonitor function
mspWarning(dataAndFlags)The R code to build and save the simulation graphics
from the paper are in the inst/mspGraphsGrid.R file.
This work is supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582 and by the National Science Foundation PFI:BIC Award No: 1632227.