Automated-Predictive-Maintenance

Overview

This project implements an Automated Predictive Maintenance system leveraging Machine Learning (ML) and IoT sensor data to detect potential failures before they happen. By analyzing sensor readings, the model predicts equipment anomalies and failures, reducing downtime and optimizing maintenance schedules.

Key Features

✅ Data Ingestion & Preprocessing – Extracts real-time IoT sensor data and processes it for analysis.
✅ Feature Engineering – Extracts meaningful insights from sensor readings.
✅ ML Model Training & Evaluation – Uses advanced machine learning models for predictive maintenance.
✅ Automated Pipeline – End-to-end workflow from data collection to prediction using MLOps principles.
✅ Visualization & Reporting – Generates actionable insights for maintenance teams.

Tech Stack

Python – Data processing & model training
AWS / Cloud Services – Scalable deployment (if applicable)
ML Frameworks – Scikit-learn, TensorFlow/PyTorch (depending on implementation)
SQL / NoSQL Databases – Data storage & retrieval

Dataset: METROP3

This project utilizes the METROP3 dataset, which consists of IoT sensor readings from industrial equipment. The dataset contains time-series data on temperature, vibration, pressure, and other key indicators used to predict failures. Ensure you have access to the dataset before running the pipeline.

How It Works

1. Collect sensor data from IoT-enabled devices.
2. Preprocess & analyze data for trends & anomalies.
3. Train ML models to predict failures based on historical patterns.
4. Deploy & monitor the system for real-time predictions.

Use Cases

Manufacturing – Detect machine failures before breakdowns.
Energy & Utilities – Monitor turbines, transformers, and grid components.
Aviation & Automotive – Predict maintenance needs in aircraft & vehicles.
Healthcare Equipment – Ensure reliability of critical medical devices.

Get Started

Clone the repository and install dependencies:

git clone https://github.com/jagguvarma15/Automated-Predictive-Maintenance.git
cd Automated-Predictive-Maintenance
pip install -r requirements.txt

Run the predictive maintenance pipeline:

python main.py

Note

This project is not fully accomplished and currently under halt for certain period of time.