R/data.R

In IGPFrailty: Inverse Gaussian Process Degradation Models with Frailty

#' GaAs Laser Degradation Dataset
#'
#' Repeated-measures degradation data on 15 Gallium Arsenide (GaAs) laser devices
#' tested at 80 degrees Celsius over 4,000 hours of operation with 16 equidistant inspection intervals.
#'
#' @format A data frame with 255 rows and 5 variables:
#' \describe{
#'   \item{unit}{Integer unit identifier (1 to 15).}
#'   \item{unit_id}{Original unit ID code (101 to 115).}
#'   \item{hours}{Inspection time in hours (0, 250, 500, ..., 4000).}
#'   \item{t}{Inspection time in thousands of hours (0.0 to 4.0).}
#'   \item{increase}{Percentage increase in operating current (\%), with failure defined at 10\%.}
#' }
#'
#' @source
#' Meeker, W. Q., & Escobar, L. A. (1998). \emph{Statistical Methods for Reliability Data}. John Wiley & Sons.
#'
#' Morita, L. H. M., Tomazella, V. L. D., Balakrishnan, N., Ramos, P. L., Ferreira, P. H., & Louzada, F. (2021).
#' Inverse Gaussian process model with frailty term in reliability analysis. \emph{Quality and Reliability Engineering International},
#' 37(2), 763-784. \doi{10.1002/qre.2762}.
#'
#' @examples
#' data(laser)
#' head(laser)
"laser"

#' Fatigue Crack Growth Degradation Dataset
#'
#' Experimental fatigue crack length measurements on 21 alloy specimens
#' subjected to cyclic stress testing up to 0.12 million cycles (120 thousand cycles).
#'
#' @format A data frame with 262 rows and 6 variables:
#' \describe{
#'   \item{specimen}{Integer specimen identifier (1 to 21).}
#'   \item{megacycles}{Inspection time in millions of cycles (0.00 to 0.12).}
#'   \item{cycles}{Inspection time in cycles (0 to 120,000).}
#'   \item{t}{Inspection time in thousands of cycles (0 to 120).}
#'   \item{inches}{Crack length measurement in inches (initial crack 0.90 inches, failure at 1.60 inches).}
#'   \item{deg}{Transformed cumulative degradation \eqn{D^*(t) = \log(D(t) / 0.90)}, with failure threshold \eqn{\rho = 0.5754}.}
#' }
#'
#' @source
#' Lu, C. J., & Meeker, W. Q. (1993). Using degradation measures to estimate a time-to-failure distribution.
#' \emph{Technometrics}, 35(2), 161-174. \doi{10.1080/00401706.1993.10485038}.
#'
#' Morita, L. H. M., Tomazella, V. L. D., Balakrishnan, N., Ramos, P. L., Ferreira, P. H., & Louzada, F. (2021).
#' Inverse Gaussian process model with frailty term in reliability analysis. \emph{Quality and Reliability Engineering International},
#' 37(2), 763-784. \doi{10.1002/qre.2762}.
#'
#' @examples
#' data(crack)
#' head(crack)
"crack"