fboldt/bearing-fault-diagnosis

Bearing Fault Diagnosis Framework

This framework is designed for fault diagnosis in bearings using vibration data and machine learning algorithms.

Installation Guide

Before you start, it's recommended to create a virtual environment to manage your dependencies. Below are two methods for installing the necessary dependencies: automatic and manual.

1. Automatic Installation (Recommended)

To automatically install the dependencies, use the provided installation script. This method is the easiest and ensures that all required libraries are installed correctly.

Steps:

1. Clone the repository:

   git clone https://github.com/fboldt/bearing-fault-diagnosis.git
   cd bearing-fault-diagnosis
   
   

2. Create a virtual environment (optional but recommended):

   python3 -m venv env
   source env/bin/activate  # On Windows: env\Scripts\activate
   
   

3. Run the installation script:

   bash install_dependencies.sh
   
   

The script will prompt you to select the installation type:

• Option 1: Install minimal dependencies
• Option 2: Install dependencies for running a CNN
• Option 3: Install all dependencies

2. Manual Installation

If you prefer to install the dependencies manually, follow the steps below.

Steps:

1. Install the minimal dependencies:

   pip install numpy scipy requests pyunpack rarfile scikit-learn imblearn PyWavelets
   
   

2. If you want to run a CNN, additionally install TensorFlow:

   pip install tensorflow
   
   

Running Experiments

To run an experiment, use the provided experimenter_kfold.py script.

```bash
python experimenter_kfold.py