Predictive Maintenance for Equipment

This project focuses on predictive maintenance to forecast equipment failures before they occur. By analyzing sensor data and operational parameters, the model helps in scheduling maintenance tasks at optimal times, minimizing unplanned downtimes and reducing maintenance costs.

Features

• Uses advanced machine learning algorithms like Random Forest, XGBoost, and Neural Networks to predict equipment failures.
• Analyzes sensor data, operational conditions, and historical maintenance logs to detect early signs of wear and tear.
• Generates maintenance schedules based on predictive insights, helping to avoid unplanned downtimes.
• Provides real-time alerts and visualizations to monitor equipment health and maintenance needs.

Installation

To set up the predictive maintenance system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/predictive-maintenance.git
   cd predictive-maintenance

2. Set Up Virtual Environment (Optional but Recommended)

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

3. Install Required Packages

   pip install -r requirements.txt

Usage

To start the predictive maintenance analysis, run the main script with your sensor data:

python maintenance_forecasting.py --input