Learn how to implement predictive maintenance using TinyML and Edge Computing to detect motor anomalies before they fail.
Why Edge AI for Motor Vibration?
In the era of Industry 4.0, traditional maintenance is being replaced by Predictive Maintenance. By using Edge AI, we can process high-frequency vibration data directly on the device, reducing latency and bandwidth costs while ensuring real-time anomaly detection.
Key Benefits:
- Low Latency: Immediate response to critical vibration spikes.
- Privacy & Security: Data stays on the local hardware.
- Bandwidth Efficiency: Only insights (not raw data) are sent to the cloud.
The Technical Workflow
To build a robust Motor Vibration Analysis system, we follow these essential steps:
- Data Acquisition: Using high-precision accelerometers (like the MPU6050 or ADXL345) to capture 3-axis vibration data.
- Feature Engineering: Converting raw time-domain signals into frequency-domain features using FFT (Fast Fourier Transform).
- Model Deployment: Deploying a TinyML model optimized for microcontrollers (ESP32, Arduino, or Raspberry Pi Pico).
Sample Implementation Logic
Below is a conceptual snippet for processing vibration data on an Edge device using a pre-trained model library:
#include <EdgeML_Library.h>
void setup() {
Serial.begin(115200);
InitializeAccelerometer();
LoadTinyMLModel(motor_vibration_model);
}
void loop() {
float x, y, z;
ReadVibrationData(&x, &y, &z);
// Run Inference on the Edge
int result = InferAnomaly(x, y, z);
if (result == ANOMALY_DETECTED) {
TriggerEmergencyStop();
Serial.println("Warning: High Vibration Detected!");
}
delay(10); // High-frequency sampling
}
