Reliability Analysis with R: A Companion Guide

A companion reference guide to the ReliaLearnR interactive tutorials, covering RAM analysis, life data analysis, reliability testing, repairable systems, and reliability block diagrams, with additional chapters on interactive visualization with ReliaPlotR and web-based analysis with ReliaShiny.

Author

Paul Govan

Published

May 28, 2026

Preface

Reliability Analysis with R: A Companion Guide

This book is a companion reference guide to the ReliaLearnR interactive learning modules. Where the tutorials offer hands-on practice with immediate feedback and interactive widgets, this book provides readable, searchable, printable chapters that cover the same material in a narrative format — plus two additional chapters on the wider ReliaLearnR ecosystem.

How to Use This Book and the Tutorials Together

The interactive tutorials and this book cover the same core content but serve different purposes:

Feature	Interactive Tutorials	This Book
Instant code feedback	Yes	No
Quiz questions	Yes	Review questions
Interactive sliders	Yes	Static equivalents
Searchable / printable	No	Yes
Copy-paste code	Limited	Full
Ecosystem chapters (ReliaPlotR, ReliaShiny)	No	Yes

Recommended workflow: Read a chapter here first to understand the concepts and see the code, then open the corresponding tutorial to practice. The chapter order mirrors the tutorial progression.

The ReliaLearnR Ecosystem

ReliaLearnR is one part of a broader suite of R packages for reliability analysis:

Package	Role
ReliaLearnR	Interactive learning modules
WeibullR	Weibull life data analysis
ReliaGrowR	Reliability growth and repairable systems
WeibullR.ALT	Accelerated life testing
ReliaPlotR	Interactive reliability plots
ReliaShiny	Web-based reliability analysis app

Installation

Install the core packages needed to run the code in this book:

install.packages(c(
  "ReliaLearnR",
  "WeibullR",
  "ReliaGrowR",
  "WeibullR.ALT",
  "DiagrammeR",
  "ReliaPlotR",
  "ReliaShiny"
))

Chapter Overview

RAM — Foundational reliability, availability, and maintainability metrics.
Reliability Block Diagrams — Series, parallel, mixed, k-out-of-n systems, and fault tree analysis.
Life Data Analysis — Weibull distribution, censoring, MRR vs. MLE, and contour plots.
Reliability Growth Analysis — Duane and Crow-AMSAA growth models, test planning, and forecasting.
Accelerated Life Testing — Arrhenius and Power Law acceleration models for compressed test time.
Repairable Systems — Power Law Process, MCF, and fleet-level analysis.
Interactive Plots with ReliaPlotR — Publishable interactive reliability charts.
Web-Based Analysis with ReliaShiny — Point-and-click reliability analysis.

The book also includes an Appendix (helper function reference) and a Glossary (key symbols and formulas).

About the Author

Paul Govan is the author of ReliaLearnR, ReliaGrowR, ReliaPlotR, and ReliaShiny. He can be reached at paul.govan2@gmail.com.

# Preface {.unnumbered} This book is a companion reference guide to the **ReliaLearnR** interactive learning modules. Where the tutorials offer hands-on practice with immediate feedback and interactive widgets, this book provides readable, searchable, printable chapters that cover the same material in a narrative format — plus two additional chapters on the wider ReliaLearnR ecosystem. ## How to Use This Book and the Tutorials Together The interactive tutorials and this book cover the same core content but serve different purposes: | Feature | Interactive Tutorials | This Book | |---|---|---| | Instant code feedback | Yes | No | | Quiz questions | Yes | Review questions | | Interactive sliders | Yes | Static equivalents | | Searchable / printable | No | Yes | | Copy-paste code | Limited | Full | | Ecosystem chapters (ReliaPlotR, ReliaShiny) | No | Yes | **Recommended workflow**: Read a chapter here first to understand the concepts and see the code, then open the corresponding tutorial to practice. The chapter order mirrors the tutorial progression. ## The ReliaLearnR Ecosystem ReliaLearnR is one part of a broader suite of R packages for reliability analysis: | Package | Role | |---|---| | **ReliaLearnR** | Interactive learning modules | | **WeibullR** | Weibull life data analysis | | **ReliaGrowR** | Reliability growth and repairable systems | | **WeibullR.ALT** | Accelerated life testing | | **ReliaPlotR** | Interactive reliability plots | | **ReliaShiny** | Web-based reliability analysis app | ## Installation Install the core packages needed to run the code in this book: ```{r} #| eval: false install.packages(c( "ReliaLearnR", "WeibullR", "ReliaGrowR", "WeibullR.ALT", "DiagrammeR", "ReliaPlotR", "ReliaShiny" )) ``` ## Chapter Overview 1. **RAM** — Foundational reliability, availability, and maintainability metrics. 2. **Reliability Block Diagrams** — Series, parallel, mixed, k-out-of-n systems, and fault tree analysis. 3. **Life Data Analysis** — Weibull distribution, censoring, MRR vs. MLE, and contour plots. 4. **Reliability Growth Analysis** — Duane and Crow-AMSAA growth models, test planning, and forecasting. 5. **Accelerated Life Testing** — Arrhenius and Power Law acceleration models for compressed test time. 6. **Repairable Systems** — Power Law Process, MCF, and fleet-level analysis. 7. **Interactive Plots with ReliaPlotR** — Publishable interactive reliability charts. 8. **Web-Based Analysis with ReliaShiny** — Point-and-click reliability analysis. The book also includes an **Appendix** (helper function reference) and a **Glossary** (key symbols and formulas). ## About the Author Paul Govan is the author of ReliaLearnR, ReliaGrowR, ReliaPlotR, and ReliaShiny. He can be reached at paul.govan2@gmail.com.