Industrial safety and unexpected machine failures remain major challenges for small and medium-scale workshops, where expensive monitoring solutions are often inaccessible. This project presents a low-cost, edge-intelligent system designed to improve workplace safety while enabling predictive maintenance using affordable sensing and embedded processing.
The system integrates multiple sensors to monitor environmental and operational conditions such as temperature, vibration, and proximity. Data collected from these sensors is processed locally on a microcontroller, allowing the system to detect abnormal patterns and unsafe conditions in real time without relying on cloud infrastructure. When anomalies are identified, the system triggers immediate alerts through visual and audio indicators, ensuring rapid response and risk reduction.
By combining embedded intelligence, sensor fusion, and real-time monitoring, the solution demonstrates how Industry 4.0 concepts can be adapted for resource-constrained environments. The prototype highlights scalability, affordability, and ease of deployment, making it suitable for small workshops, educational labs, and entry-level industrial settings.
This project emphasizes accessibility and practical impact by showcasing how edge-based monitoring can enhance safety, reduce downtime, and support preventive decision-making while maintaining minimal hardware and computational requirements.
What's next for Low cost Edge AI Industrial Safety & Maintenance System
Built With
- arduino
- cloudvisualization
- embeddedc
- proximitysensor
- temperaturesensor
- tinyml
- vibrationsensor
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