sdp

Overview

The goal of sdp is to provide engineers with practical guidance on how to analyze data using common degradation models: Wiener process, Gamma process, and Inverse Gaussian process. For each degradation process, we provide data simulation generation, statistical inference, and remaining useful life prognostics.

Installation

You can install the development version of sdp from GitHub with:

# install.packages("devtools")
devtools::install_github("liangliangzhuang/sdp")

Get started

library(sdp)
## basic example code

sdp functions fall into five main categories:

• “Simulation” which can simulate a group of degradation models, see sim_dat().

• “Inference” which contains maximum likelihood estimator (MLE) and Bayesian method, see sta_infer().

• “Ploting” which contains several visualization functions, mainly for plotting degradation paths, reliability and remaining useful life. See plot_path(), RUL_plot(), Reliability_plot(), Reliability_cowplot(), and RUL_3D_density().

• “Others” which contains some useful functions. See cumsub(), and acc_stress().

Articles

• Real data analysis

• Simulation data analysis

Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.