Identify Leaking Gear Box By Edge Impulse

The Identify Leaking Gear Box By Edge Impulse project operates by deploying a machine learning model directly onto an edge device or microcontroller to monitor industrial gearboxes for signs of fluid leaks. The system begins by interfacing with specialized sensors placed strategically around the industrial gearbox setup within a coal handling plant environment. These sensors continuously capture physical operational data—such as vibration patterns, acoustic signatures, or visual indicators depending on the sensor type—and feed this raw telemetry into the compatible microcontroller or edge device. Prior to deployment, an advanced TinyML classification model is trained using Edge Impulse on datasets representing both normal operational states and failure states associated with gearbox leaks. Once flashed onto the hardware, the microcontroller executes real-time inference on the incoming sensor streams without needing constant cloud connectivity. When the machine learning algorithm detects patterns that cross a high-confidence threshold for a leak, the edge device triggers automated responses, which can include sounding an alarm, displaying a warning indicator, or interfacing with industrial control systems to safely stop the heavy machinery before catastrophic failure occurs.