Predictive Maintenance for Manufacturing Equipment

1. Challenge Overview

This challenge focuses on developing an AI-driven predictive maintenance system that monitors manufacturing equipment using real-time sensor data. The solution should predict potential failures, enabling proactive maintenance scheduling to minimize unplanned downtime and reduce operational costs.

2. Problem Statement

Build a predictive maintenance AI system that:

• Uses time-series data from IoT sensors (e.g., temperature, vibration, pressure) to predict equipment failures.
• Supports two interfaces:
  • Maintenance Dashboard – Displays machine health status, predicted failures, and maintenance schedules.
  • Analytics Engine – Processes sensor data, applies machine learning models, and generates failure predictions.
• Stores equipment sensor data in a database (SQL or NoSQL).
• Bounty Points for real-time alert integration via SMS/email for critical failures.

3. Technical Approach

Participants must implement:

• Machine Learning Model – Train a predictive maintenance model using algorithms such as LSTM, Random Forest, or XGBoost.
• Data Ingestion & Processing – Use IoT sensor streams or historical datasets to preprocess and normalize input features.
• Database Integration – Store and retrieve real-time sensor readings and predictions using SQL (PostgreSQL, MySQL) or NoSQL (MongoDB, InfluxDB).
• Maintenance Dashboard – Build an interactive dashboard (React, Flask, or Streamlit) to:
  • Display real-time machine health and failure risk levels.
  • Schedule maintenance based on AI predictions.
  • View historical equipment performance trends.
• Real-time Alert System – Implement notifications via Twilio API to alert maintenance teams when failure risk is high.
• Performance Optimization – Measure model accuracy using evaluation metrics like Precision, Recall, and F1 Score.

4. Maintenance Dashboard Features

The dashboard should allow users to:

• Monitor live sensor data streams from manufacturing equipment.
• View failure risk levels (e.g., Low, Medium, High) for each machine.
• Receive automated alerts for high-risk conditions.
• Access predictive maintenance reports with historical data.
• Integrate with existing manufacturing software (optional).

5. References

• IoT Sensor Data Simulation: NASA Turbofan Engine Dataset
• Predictive Maintenance Models: Azure Predictive Maintenance Guide
• LSTM for Time-Series Forecasting: TensorFlow LSTM Guide
• Twilio API for Notifications: Twilio Docs
• InfluxDB for Time-Series Data: InfluxDB Docs

6. Evaluation Criteria

Submissions will be assessed based on:

• Prediction Accuracy (40%) – Model performance in predicting failures before they occur.
• Model Efficiency (20%) – Computational performance and response time.
• User Experience (20%) – Ease of interaction with the dashboard.
• Scalability & Real-time Integration (10%) – Ability to process large sensor data streams.
• Code Quality & Documentation (10%) – Well-structured, maintainable code with clear instructions.

7. Deliverables

• Source Code & README – Well-documented code with setup instructions.
• Working Predictive Maintenance System – Functional model with dashboard and alerts.
• Evaluation Report – Model performance comparison using metrics like Precision, Recall, F1 Score.
• Demo Video (Optional) – Short video demonstrating system functionalities.

8. Implementation Guidelines

• Train a predictive maintenance model using LSTM, Random Forest, or XGBoost.
• Process and store sensor data in SQL/NoSQL databases.
• Develop a user-friendly maintenance dashboard with real-time monitoring.
• Implement notification alerts for potential failures.
• Support multi-machine monitoring for scaling across factories.