In the era of Industry 4.0, unplanned downtime can cost manufacturers thousands of dollars per hour. One of the most common causes of machinery failure is bearing faults. Traditionally, monitoring these required expensive sensors and constant cloud connectivity. However, with the rise of Edge AI, we can now detect anomalies directly on the hardware.
Why Use Edge Processing for Fault Detection?
Deploying Machine Learning models on Edge processing boards (such as Arduino Nicla Vision, Raspberry Pi, or ESP32) offers several advantages:
- Low Latency: Real-time analysis without waiting for cloud processing.
- Bandwidth Efficiency: Only critical alerts are sent to the server.
- Data Privacy: Raw vibration data stays on the local device.
The 4-Step Workflow for AI-Driven Monitoring
1. Data Acquisition
Using high-frequency accelerometers, we collect vibration signatures. Healthy bearings produce a consistent, low-amplitude signal, while faulty ones exhibit specific harmonic peaks.
2. Feature Extraction
Raw data is processed using Fast Fourier Transform (FFT) or Power Spectral Density (PSD) to convert time-domain signals into the frequency domain, making faults easier to identify.
3. Training the TinyML Model
Using frameworks like Edge Impulse or TensorFlow Lite, we train a neural network to recognize patterns such as Inner Race Faults, Outer Race Faults, or Ball Damage.
4. On-Device Inference
The optimized model is deployed onto an Edge board. The device constantly monitors the "health score" of the bearing and triggers an alert if an anomaly is detected.
Practical Implementation
To implement Predictive Maintenance, engineers often use an IMU sensor paired with an ARM Cortex-M series processor. By identifying the Fundamental Train Frequency (FTF) and other defect frequencies early, you can schedule repairs before a catastrophic failure occurs.
