Reliability engineering toolkit for Python - https://reliability.readthedocs.io/en/latest/

Statistical methods and visualizations often used in reliability engineering including the well-known weibull analysis and Monte Carlo simulations

Estimating life expectancy for users' hard drive disks thanks to Python scripts and a statistic analysis based on BackBlaze open data and our Weibull curve model

Proyecto Grado Tecnologia Electricidad Industrial

Python tool to predict failure rates and reliability in the smart meter industry using Weibull analysis.

This repository explores the integration of Machine Learning with the Weibull Distribution to improve the accuracy of Remaining Useful Life (RUL) estimations. By treating the Weibull scale parameter ( λ ) as a dynamic target for regression, these projects bridge the gap between statistical reliability analysis and modern data-driven maintenance.

Interactive Reliability Probability Plots

Fiabilité Prédictive et Stress-Test d'une Infrastructure Cloud

End-to-end workflow on synthetic accelerated life test (ALT) data: dataset generation, Kaplan–Meier survival analysis, Weibull-2P modeling, and Arrhenius temperature acceleration. Includes Py scripts, Jupyter notebooks, plots, and CSV outputs.

A Shiny App for Reliability Analysis

These are graphical plots of reliability data which are quick and useful visual test to determine if a particular model is consistent with an observed data from an operating system or manufacturing equipment

Reliability engineering in Python: life data analysis, reliability growth, accelerated testing, RBD, FMEA and fault trees — with the method decisions written down where the code makes them.

Learning Modules for Reliability Analysis