Haritha Vemuri

Inspiration

Predictive maintenance is a crucial need in modern industrial operations where unplanned equipment failures can lead to costly downtimes. Inspired by the integration of AI in Industry 4.0, we aimed to build a system that can proactively predict failures using real-time sensor data—boosting reliability, efficiency, and safety.

What it does

The AI Predictive Maintenance platform uses sensor data to predict potential equipment failures. It processes uploaded CSV files, validates the data, applies a trained Random Forest model for prediction, and provides visual analytics to help maintenance teams take preemptive action.

Data Processing: Validates and parses raw sensor data.
AI Prediction: Uses a Random Forest Classifier to predict failures.
Visual Analytics: Generates interactive charts and insights for users.

How we built it

Frontend: HTML, CSS, JavaScript
Backend: Python (Flask)
Machine Learning: Random Forest using Scikit-learn
Visualization: Chart.js for rendering prediction insights
Deployment: Netlify for quick and user-friendly access

Challenges we ran into

Designing a user-friendly interface for uploading and validating sensor data
Dealing with noisy or incomplete datasets during model training
Integrating real-time visual analytics with backend predictions
Ensuring the model handles a wide variety of sensor formats

Accomplishments that we're proud of

Successfully developed an end-to-end system from data upload to failure prediction and visualization
Integrated a robust Random Forest model with high accuracy
Built a platform that is both technical and easy to use for non-technical users

What we learned

How to implement machine learning in real-world applications
Importance of clean data and preprocessing for effective model performance
Skills in full-stack development, from frontend UI/UX to backend AI model integration
Visualization techniques to enhance data-driven decision-making