Master Thesis

Data-Based Generation of Reliability Models

Aim: Design and implementation of methods for automated derivation of reliability models from data (e.g. A fault tree).

Objectives: Design a reliability model to produce streams of data. Analyze data for patterns using artificial intelligence and/or machine learning. Derive and reproduce the reliability model from the data. Compare the original reliability model and reproduced reliability model and evaluate the method of automated derivation.

Download Thesis Report

Installation

Use python 3.12 to run the application.

python -m venv venv
source venv/bin/activate

pip install -r requirements.txt

Run analysis

python analysisFT.py

Run analysis

python analysisFT.py

Run proxel simulation

python proxel_example.py

Dockerized app

Build container

docker build -t fault-tree-analysis .

Run container

docker run -it --rm fault-tree-analysis

Using docker desktop you can inspect generated files such as the time series data, truth table, and the graphs in png format in the container.