WeibullR.learnr: An Interactive Introduction to Life Data Analysis

Statement of Need

Life data analysis is the study of how systems, from machines to people, perform over time. Life data includes lifespans, failure rates, and repair or replacement timelines. While various learning resources exist, many rely on proprietary software that can be inaccessible to students and early-career professionals due to cost constraints.

WeibullR.learnr (Govan 2023a) is an open-source collection of interactive learning modules, exercises, and functions designed for introductory life data analysis. The primary goal of this project is to introduce fundamental concepts while providing an open-source alternative for analyzing life data. The target audience includes beginner practitioners and university students.

Description

WeibullR.learnr is written in R (R Core Team 2023) and utilizes WeibullR (Silkworth and Symynck 2022), an R package for Life Data Analysis based on the methodologies of Waloddi Weibull (Abernethy 1993), and learnr (Aden-Buie et al. 2023), a framework for building interactive learning modules in R.

Currently, three primary learning modules are available. These modules are independent and can be completed in any order. They are designed to be plug-and-play, but users can modify them by forking the repository.

WeibullR.learnr() provides an interactive introduction to Life Data Analysis. The learning objectives include basic Weibull analysis, censoring techniques such as right and interval censored data, different types of Weibull models including the 2P Weibull, 3P Weibull, and Weibayes model, parameter estimation methods Median Rank Regression (MRR) and Maximum Likelihood Estimation (MLE), and data visualization methods such as Probability Plots and Contour Plots. The estimated duration for this module is approximately 2 hours.

RAMR.learnr() is a quick reference for common Reliability, Availability, and Maintainability (RAM) concepts. The learning objectives include the basic concepts and application of Reliability, Availability, Mean Time to Repair (MTTR), Mean Time to Failure (MTTF), Mean Time Between Failures (MTBF), Failure Rate, Probability of Failure, and \(B_n\) or \(L_n\) life. The estimated duration of this module is about 1 hour.

TestR.learnr() provides an interactive introduction to Reliability Testing. The learning objectives include defining key reliability growth concepts, including Crow-AMSAA and Duane models, fitting a reliability growth model to real-world data using R, interpreting reliability growth plots and identifying trends, applying the Crow-AMSAA model to assess reliability growth, explaining fundamental concepts of accelerated life testing, including the use of Arrhenius and Power Law Models, conducting an accelerated life test with real-world datasets, utilizing R for analysis, analyzing plots that illustrate the relationships in accelerated life testing, identifying key patterns and data trends, and utilizing Arrhenius and Power Law models to evaluate the impact of stress factors on product reliability. The estimated duration of this module is about 2 hours.

The modules can also be accessed in a browser at WeibullR.learnr, RAMR.learnr, and TestR.learnr.

Several helper functions for common RAM calculations are also included. These functions make it easy to apply the concepts covered in this module.

• rel() - reliability function
• avail() - availability function
• mttf() - mean time to failure
• mtbf() - mean time between failure
• serv() - serviceability factor
• fr() - failure rate

The project documentation includes installation instructions for WeibullR.learnr and the required dependencies, examples of running the programs, and references to previous work used to build the modules. The documentation also references more resources for learners looking for expanded applications. These resources include WeibullR.plotly (Govan 2023b), a R package for interactive Weibull probability plots, and WeibullR.shiny (Govan 2023c), a shiny (Chang et al. 2022) web application for Life Data Analysis.

This project was inspired by a well-established Reliability Program at a major technology company. However, reliance on proprietary software limited accessibility and led to outdated learning materials as software evolved. By providing open-source alternatives, this project aims to reach a broader audience and foster a community of collaboration and innovation.

Users are encouraged to explore the modules and contribute to the project. Contributions can be made via Issues and Pull Requests in the repository, which includes a Contributor Code of Conduct.

Acknowledgements

The author acknowledges the creators of the original Reliability Program that inspired this initiative.

References

Abernethy, Robert B. 1993. The New Weibull Handbook. R.B. Abernethy; Distributed by GULF Pub. Co. https://cir.nii.ac.jp/crid/1130282270976797824.

Aden-Buie, Garrick, Barret Schloerke, JJ Allaire, and Alexander Rossell Hayes. 2023. Learnr: Interactive Tutorials for r. https://CRAN.R-project.org/package=learnr.

Chang, Winston, Joe Cheng, JJ Allaire, Carson Sievert, Barret Schloerke, Yihui Xie, Jeff Allen, Jonathan McPherson, Alan Dipert, and Barbara Borges. 2022. Shiny: Web Application Framework for r. https://CRAN.R-project.org/package=shiny.

Govan, Paul. 2023a. WeibullR.learnr: An Interactive Introduction to Life Data Analysis. https://CRAN.R-project.org/package=WeibullR.learnr.

———. 2023b. WeibullR.plotly: Interactive Weibull Probability Plots with ’WeibullR’. https://CRAN.R-project.org/package=WeibullR.plotly.

———. 2023c. WeibullR.shiny: A ’Shiny’ App for Weibull Analysis from ’WeibullR’. https://CRAN.R-project.org/package=WeibullR.shiny.

R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Silkworth, David, and Jurgen Symynck. 2022. WeibullR: Weibull Analysis for Reliability Engineering. https://CRAN.R-project.org/package=WeibullR.