This repository holds the source code used for the work on the paper An End-to-End Pipeline for Uncertainty Quantification and Remaining Useful Life Estimation: An Application on Aircraft Engines

This repository is the work of Marios Kefalas, PhD candidate at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands.

To install the pipeline you can do the following:

• Clone the repository

git clone https://github.com/MariosKef/RULe.git ~/rule

• cd to the directory of the cloned repository

cd ~/rule

• Create the environment and install the requirements

\<path to python 3.8 binaries\> -m venv rule_env or virtualenv --python=\<path to python 3.8 binaries\> rule_env

Activate the environment:

source rule_env/bin/activate (Linux/Unix)

source rule_env/Scripts/activate (Windows)

Upgrade pip:

python -m pip install --upgrade pip

Install the requirements:

python -m pip install -r requirements.txt

Install locally to work on your own version:

cd ~/rule

python -m pip install -e .

• For the hyperparameter optimization (HPO), run:

cd ./RULe

python main.py (note: be sure to update the log_file in the contructor of mipego inside the main.py with the path of your choice and the file variable for the results file in the objective.py)

• For the training of the full model, run:

cd ./RULe

python model_training_full.py 100 (note: be sure to update the net_cfg with the cfg of your choice from the HPO (previous step). The extra command line argument (here 100) indicates the number of training epochs.)

This work is part of the research programme Smart Industry SI2016 with project name CIMPLO and project number 15465, which is partly financed by the Netherlands Organisation for Scientific Research (NWO).