Create an edge computing solution that monitors elevator motor performance, vibration, and load patterns locally to predict mechanical failures. The system will generate early warnings, reduce downtime, and ensure passenger safety without continuous cloud data transmission
Study elevator operational mechanisms and common fault types.
Research vibration and load sensors used in lift systems.
Design an edge-based architecture for real-time monitoring.
Implement predictive maintenance algorithms using sensor analytics.
Configure anomaly detection for abnormal movement patterns.
Develop alert notifications for maintenance teams.
Optimize computational load for embedded controllers.
Compare performance with traditional manual inspection methods.
Conduct simulated stress tests for overload conditions.
Evaluate downtime reduction metrics.
Document safety improvements and scalability potential.
