IoT Condition Monitoring Node
Edge ML IoT platform for industrial predictive maintenance built on STM32 ARM Cortex-M4. Features 3-axis accelerometer vibration analysis with 1024-point FFT, TinyML inference for anomaly detection, and MQTT cloud connectivity. IP67 enclosures with 24V industrial power. Detects bearing failures 6 months before breakdown with 95% accuracy.
⚡ Challenges
- •Edge ML with limited memory
- •Industrial environment reliability
- •Battery-efficient operation
✓ Outcomes
- ✓Detected 3 bearing failures before breakdown
- ✓95% classification accuracy
- ✓6-month average early warning
📖 Full Details
This IoT condition monitoring platform provides early warning of equipment degradation in industrial machinery, enabling predictive maintenance that prevents costly unplanned downtime. The embedded edge nodes combine multiple sensor types with on-device machine learning to detect anomalies before they become failures.
Each node is built around an STM32 ARM Cortex-M4 microcontroller with dedicated DSP capabilities for signal processing. Sensors include 3-axis MEMS accelerometers for vibration analysis, precision temperature sensors, humidity sensors, and current transformers for motor load monitoring. All sensor data is sampled synchronously and preprocessed on the edge device.
Vibration analysis uses FFT processing with 1024-point resolution, extracting frequency domain features that characterize bearing wear, imbalance, misalignment, and looseness. A TinyML model trained on failure mode data runs inference directly on the microcontroller, classifying equipment health state without cloud connectivity requirements.
Healthy baseline profiles are established during commissioning, with the ML model detecting deviations that indicate developing faults. Alert conditions trigger MQTT telemetry to cloud dashboards where maintenance teams receive prioritized action recommendations with remaining useful life predictions.
The cloud platform aggregates data from multiple nodes, providing fleet-wide health visibility with trend analysis, correlation detection across machines, and automated maintenance scheduling integration. Historical data supports continuous model improvement and failure pattern library expansion.
Nodes operate on industrial 24V power with battery backup, communicate over industrial Ethernet or cellular, and are packaged in IP67 enclosures rated for harsh industrial environments.
