| Title: | Reliability Growth Analysis |
|---|---|
| Description: | Modeling and plotting functions for Reliability Growth Analysis (RGA). Models include the Duane (1962) <doi:10.1109/TA.1964.4319640>, Non-Homogeneous Poisson Process (NHPP) by Crow (1975) <https://apps.dtic.mil/sti/citations/ADA020296>, Piecewise Weibull NHPP by Guo et al. (2010) <doi:10.1109/RAMS.2010.5448029>, and Piecewise Weibull NHPP with Change Point Detection based on the 'segmented' package by Muggeo (2024) <https://cran.r-project.org/package=segmented>. |
| Authors: | Paul Govan [aut, cre, cph]
|
| Maintainer: | Paul Govan <[email protected]> |
| License: | CC BY 4.0 |
| Version: | 0.1.3 |
| Built: | 2024-11-17 07:18:33 UTC |
| Source: | CRAN |
Plotting Function for Duane Analysis.
duane_plot( times, failures, plot = TRUE, point_col = "black", line_col = "black", xlab = "Cumulative Time", ylab = "Cumulative MTBF", main = "Duane Plot with Cumulative MTBF" )duane_plot( times, failures, plot = TRUE, point_col = "black", line_col = "black", xlab = "Cumulative Time", ylab = "Cumulative MTBF", main = "Duane Plot with Cumulative MTBF" )
times |
A vector of cumulative times at which failures occurred. |
failures |
A vector of the number of failures at each corresponding time in times. |
plot |
Show Duane Plot (TRUE) or hide plot (FALSE). |
point_col |
Color for the data points (default: "black"). |
line_col |
Color for the fitted line (default: "black"). |
xlab |
Label for the x-axis (default: "Cumulative Time"). |
ylab |
Label for the y-axis (default: "Cumulative MTBF"). |
main |
Title for the plot (default: "Duane Plot with Cumulative MTBF"). |
The function returns a list of the fitted linear model, Cumulative Time, Cumulative MTBF.
library(ReliaGrowR) times <- c(100, 200, 300, 400, 500) failures <- c(1, 2, 1, 3, 2) fit <- duane_plot(times, failures) summary(fit)library(ReliaGrowR) times <- c(100, 200, 300, 400, 500) failures <- c(1, 2, 1, 3, 2) fit <- duane_plot(times, failures) summary(fit)
Plotting Function for Reliability Growth Analysis
plot_rga( rga_obj, point_col = "black", line_col = "black", xlab = "Cumulative Time", ylab = "Cumulative Failures", main = "Reliability Growth Analysis" )plot_rga( rga_obj, point_col = "black", line_col = "black", xlab = "Cumulative Time", ylab = "Cumulative Failures", main = "Reliability Growth Analysis" )
rga_obj |
An object of class |
point_col |
Color for the data points (default: "black"). |
line_col |
Color for the fitted line (default: "black"). |
xlab |
Label for the x-axis (default: "Cumulative Time"). |
ylab |
Label for the y-axis (default: "Cumulative Failures"). |
main |
Title for the plot (default: "Reliability Growth Analysis"). |
The function does not return a value.
times <- c(100, 200, 300, 400, 500) failures <- c(1, 2, 1, 3, 2) result <- rga(times, failures) plot_rga(result)times <- c(100, 200, 300, 400, 500) failures <- c(1, 2, 1, 3, 2) result <- rga(times, failures) plot_rga(result)
Reliability Growth Analysis.
rga( times, failures, model_type = "Crow-AMSAA", breakpoints = NULL, conf_level = 0.95 )rga( times, failures, model_type = "Crow-AMSAA", breakpoints = NULL, conf_level = 0.95 )
times |
A vector of cumulative times at which failures occurred. |
failures |
A vector of the number of failures at each corresponding time in times. |
model_type |
The model type. Either |
breakpoints |
An optional vector of breakpoints for the |
conf_level |
The desired confidence level, which defaults to 95%. |
The function returns an object of class rga that contains the results for the model.
times <- c(100, 200, 300, 400, 500) failures <- c(1, 2, 1, 3, 2) result <- rga(times, failures) print(result)times <- c(100, 200, 300, 400, 500) failures <- c(1, 2, 1, 3, 2) result <- rga(times, failures) print(result)
Convert Weibull Data to Reliability Growth Data
weibull_to_rga(failures, suspensions = NULL)weibull_to_rga(failures, suspensions = NULL)
failures |
A vector of failure times. |
suspensions |
A vector of suspension (censoring) times. |
A data frame with times and failure counts suitable for reliability growth analysis.
failures <- c(100, 200, 200, 400) suspensions <- c(250, 350, 450) result <- weibull_to_rga(failures, suspensions) print(result)failures <- c(100, 200, 200, 400) suspensions <- c(250, 350, 450) result <- weibull_to_rga(failures, suspensions) print(result)