Virtual Booth

 Electric motors play a critical role in petroleum industry operations, operating continuously under high load and harsh conditions. However, motor failures are often detected only after severe damage has occurred, resulting in operational downtime, safety risks, energy waste, and increased maintenance costs. This project addresses the challenge of late detection of electric motor problems in industrial settings. The objective of MotorGuard AI is to introduce an early-warning system concept that detects abnormal motor behaviour before major failure occurs. The proposed solution focuses on monitoring motor behaviour patterns and identifying early signs of abnormal operation to trigger maintenance alerts. Rather than building a real industrial system, this project presents a conceptual prototype that demonstrates the monitoring logic and decision-making process behind early detection. MotorGuard AI supports Sustainable Development Goal 9 (Industry, Innovation and Infrastructure) by promoting smarter industrial maintenance, and Sustainable Development Goal 7 (Affordable and Clean Energy) by reducing energy waste caused by inefficient or damaged motors. The expected impact of this project includes improved motor reliability, reduced downtime, safer operations, and more sustainable industrial practices. Through industry collaboration and student-driven innovation, MotorGuard AI bridges education with real-world engineering challenge.