- This project is a front-end dashboard for an AI-driven platform that monitors and predicts maintenance needs for industrial robotic systems.
- The system analyzes real-time sensor data, predicts potential failures, and visualizes the health status of a fleet of robots to minimize downtime and improve operational efficiency.
- In modern industrial settings, unexpected equipment failure is a primary cause of costly downtime.
- This AI-Powered Predictive Maintenance System is designed to address this challenge for industrial and warehouse robotics.
- By integrating with IoT sensors on each robotic unit, the platform uses machine learning models to analyze real-time data streams (e.g., temperature, vibration, load, operating hours).
- It predicts the probability of component failure, allowing maintenance to be scheduled proactively before a breakdown occurs.
- This dashboard serves as the central interface for monitoring the system, visualizing robot health, and managing maintenance tasks.
- Centralized Monitoring: A comprehensive dashboard to view the status of the entire robotic fleet at a glance.
- Real-Time Data Visualization: Live charts display critical sensor metrics like temperature, vibration, and current for individual robots.
- AI-Driven Failure Prediction: Each robot card displays a "Failure Probability" score, calculated by a simulated AI model based on current operating conditions.
- Dynamic Status Updates: Robots are automatically categorized as Operational, Warning, or Critical based on their failure probability, with visual cues for high-risk units.
- Task Management: A prioritized list of generated maintenance tasks, from routine checks to critical emergency repairs.
- System Controls: Interactive buttons to start/stop the monitoring simulation, refresh data, and generate console reports.
- Responsive Design: The interface is fully responsive and accessible on various screen sizes, from large monitors to tablets.
- This front-end demonstration is built with standard web technologies, ensuring accessibility and ease of use.
- HTML5: For the structure and content of the dashboard.
- CSS3: For modern styling, including gradients, shadows, animations, and a responsive grid layout.
- JavaScript (ES6+): For all the dynamic logic, including data simulation, DOM manipulation, and event handling.
- This prototype simulates a real-world predictive maintenance environment.
- Data Simulation: The JavaScript includes a function (simulateDataUpdate) that mimics the data stream from IoT sensors on multiple robots. Every few seconds, it generates new, slightly randomized values for temperature, vibration, load, etc.
- AI Model Simulation: The core of the "prediction" is a simplified algorithm within the simulation function. It calculates a failureProbability score based on a set of rules (e.g., probability increases if temperature exceeds 85°C or vibration passes a certain threshold). In a full implementation, this would be replaced by a genuine machine learning model.
- Dynamic UI Rendering: The dashboard dynamically updates based on the simulated data. Robot cards change color, metrics update, and charts redraw themselves with new data points without requiring a page refresh.
- Alerting & Tasking: When a robot's status becomes 'Warning' or 'Critical', the system generates user-facing alerts and would, in a full version, trigger the creation of a maintenance task managed by a multi-agent system.
- Because this is a self-contained front-end prototype, no complex installation is required.
- Clone the repository (or download the source code).
git clone https://github.com/your-username/predictive-maintenance-dashboard.git - Navigate to the project directory.
- Open the index.html file in any modern web browser (like Chrome, Firefox, or Edge).
- That's it! The dashboard will be running locally in your browser.
- Start System: Click the 🚀 Start System button to begin the real-time data simulation. You will see the status cards, robot metrics, and charts begin to update.
- Stop System: Click the 🛑 Stop System button to pause the simulation.
- Generate Report: Click 📊 Generate Report to print a summary of the current robot statuses and pending tasks to the browser's developer console (Press F12 to open).
- Observe: Watch how the Failure Probability changes based on the simulated sensor data and how the robot status (Operational, Warning, Critical) updates in response.
- Fork the repository
- Create a feature branch (git checkout -b feature/amazing-feature)
- Commit your changes (git commit -m 'Add amazing feature')
- Push to the branch (git push origin feature/amazing-feature)
- Open a Pull Request
- This project is distributed under the MIT License. See the LICENSE file for more information.