ZeMA Testbed Bayesian Machine Learning

This is supported by European Metrology Programme for Innovation and Research (EMPIR) under the project Metrology for the Factory of the Future (Met4FoF), project number 17IND12.

Purpose

This is an implementation of Bayesian machine learning for the ZEMA dataset on condition monitoring of a hydraulic system.

Getting started

The easiest way to get started is navigating to the folder in which you want to create a virtual Python environment (venv), create one, activate it, install all necessary dependencies from PyPI.org and then call main.py with preset parameters.

Set up a virtual environment

For the motivation of creating a virtual environment for your installation of the agents check the official Python docs on that topic . The commands differ slightly between Windows and Mac/Linux .

Create a venv Python environment on Windows

In your Windows PowerShell execute the following to set up a virtual environment in a folder of your choice.

PS C:> cd C:\LOCAL\PATH\TO\ENVS
PS C:\LOCAL\PATH\TO\ENVS> py -3 -m venv ZeMA_Bayesian_Machine_Learning_venv
PS C:\LOCAL\PATH\TO\ENVS> ZeMA_Bayesian_Machine_Learning_venv\Scripts\activate

Create a venv Python environment on Mac & Linux

In your terminal execute the following to set up a virtual environment in a folder of your choice.

$ cd /LOCAL/PATH/TO/ENVS
$ python3 -m venv ZeMA_Bayesian_Machine_Learning_venv
$ source ZeMA_Bayesian_Machine_Learning_venv/bin/activate

Install dependencies via pip

Once you activated your virtual environment, you can install all required dependencies from the root of your repository via:

pip install -r requirements.txt

Collecting [..]
Successfully installed [...]

Launch main.py with preset parameters

To execute the software properly, we have to add its current location temporarily to the runtime Python path. Again the commands differ slightly between Windows and Mac/Linux.

Launch main.py on Windows

To start the algorithm after activating your venv and installing the dependencies,execute the following in the Powershell from the root of your repository.

(ZeMA_Testbed_Bayesian_Machine_Learning_venv) PS C:> cd C:\LOCAL\PATH\TO\REPO
(ZeMA_Testbed_Bayesian_Machine_Learning_venv) PS C:\LOCAL\PATH\TO\REPO> $env:PYTHONPATH += ";$pwd"
(ZeMA_Testbed_Bayesian_Machine_Learning_venv) PS C:\LOCAL\PATH\TO\REPO> python ZeMA_testbed_Bayesian_machine_learning\main.py
START PROGRAM
DOWNLOAD/IMPORT DATA
Downloads / Imports and converts ADC mesurements to real SI units
DOWNLOAD DATA: starting...
[...]

Now you can watch the execution of the Bayesian inference.

Launch main.py on Mac & Linux

To start the algorithm after activating your venv and installing the dependencies,execute the following in the terminal from the root of your repository.

(ZeMA_Testbed_Bayesian_Machine_Learning_venv) $ PYTHONPATH=.:$PYTHONPATH python ZeMA_testbed_Bayesian_machine_learning/main.py 
START PROGRAM
DOWNLOAD/IMPORT DATA
Downloads / Imports and converts ADC mesurements to real SI units
DOWNLOAD DATA: starting...
[...]

Now you can watch the execution of the Bayesian inference.

References

For details about the process refer to the author Loïc Coquelin (LNE).