EdgeSense AI

EdgeSense AI was inspired by the need to prevent unexpected industrial machine failures through smart, real-time monitoring. We explored how IoT sensors, Edge Computing, and Machine Learning can work together to detect abnormal machine behavior, generate health scores, and provide early maintenance alerts. Throughout the project, we learned about sensor data acquisition, feature extraction, anomaly detection, MQTT communication, and dashboard integration. We faced challenges in integrating the different modules, handling noisy sensor data, selecting lightweight machine-learning models, and ensuring reliable communication. These challenges helped us design a modular and efficient predictive maintenance framework that aims to reduce downtime, improve machine reliability, and minimize unnecessary data transmission.

Built With

• anomaly-detection
• c/c++
• mongodb
• numpy
• pandas
• python-?-machine-learning
• skicit
• sql