Predictive Maintenance Machine Learning Model

This repository offers a comprehensive framework for predictive maintenance, leveraging machine learning techniques to anticipate equipment failures in industrial settings. It covers everything from data preprocessing and feature engineering to model training and deployment via an interactive dashboard.

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🚀 Features

• Data Preprocessing – Clean and transform raw industrial sensor data.
• Feature Engineering – Extract spectral, statistical, and health indicators.
• Model Training – Train classification (failure prediction) and regression (RUL estimation) models.
• Visualization Dashboard – Streamlit-based dashboard for real-time monitoring and visualization.
• Modular Design – Easy to extend, customize, and plug into other systems.

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📁 Repository Structure

pred-main-mod/
├── data/                   # Raw and processed datasets
├── data_tables/            # Aggregated feature tables
├── models/                 # Trained models (RandomForest, TensorFlow, etc.)
├── notebooks/              # Jupyter Notebooks for exploratory data analysis
├── scripts/                # Processing and training scripts
├── dashboard/              # Streamlit app files
├── utils/                  # Helper functions
├── README.md               # Project readme file
├── requirements.txt        # Python dependencies
└── LICENSE                 # License file

⚙️ Setup Instructions

1. Clone the Repository

First, clone the repository to your local machine:

git clone https://github.com/RealThanosP/pred-main-mod.git
cd pred-main-mod

2. Create a virtual environment

It's recommended to use a virtual environment to manage dependencies:

# Create the virtual environment
python3 -m venv venv

# Activate it (Linux/macOS)
source venv/bin/activate

# Or activate it (Windows)
venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Modify your config file accordingly

Add the configuration you wish for your data in the config.json.

5. Paste your csv like data in the data folder

Paste the data you want to make a prediction for in the data folder. Now you are all set to use the model.

6. Run the data_processing.py

Run data_processing.py and the extracted tables

🧪 Example usage from terminal:

python model_prediction.py --model models/failure_flag_model.pkl --data data_tables/new_sensor_input.csv