Factory downtime is expensive, and most teams only find out about a problem after a machine fails. For my Final Year Project, I built SmartAI Factory, a platform that lets teams see problems coming and fix them early.
It streams live temperature, vibration, and current data from industrial machines to a real-time dashboard that flags anomalies as they happen. An Autoformer-based AI model then forecasts machine behavior 15, 30, 45, and 60 minutes ahead, each with a confidence score.
What I built:
Live monitoring dashboard with anomaly-marked charts and status badges
AI forecasting across 4 time horizons
Secure device onboarding: unknown machines stay Pending until an admin approves them
Per-machine alert thresholds with in-app and email notifications
Role-based access, user management, and light/dark mode
My role: Full-stack development, UI/UX design, and AI model integration, end to end.
Result: A complete predictive-maintenance workflow, from onboarding a device to getting an alert, in one clean interface.
Open to freelance work on AI dashboards, IoT monitoring systems, and full-stack web apps. Let's talk.
A test execution and reporting solution demonstrating how automated test results can be presented through dashboards, execution summaries, screenshots, logs, and failure diagnostics. The setup helps identify failed scenarios quickly and provides better visibility into regression testing.
Skills demonstrated: Test Reporting, Regression Testing, Failure Analysis, Screenshots, Logs, Allure/HTML Reports, Test Analytics.