In the era of Industrial 4.0, monitoring motor health in real-time is crucial. However, streaming high-frequency sensor data (vibration, current, and temperature) to the cloud often leads to bandwidth congestion and high latency. The solution? On-board AI processing.
Why Use On-Board AI for Motor Data?
By implementing Edge AI, we can process raw signals directly on the microcontroller. This allows for immediate anomaly detection and significantly reduces the amount of data transmitted over the network.
Step-by-Step: Handling High-Frequency Data
- Data Acquisition: Sampling motor vibrations at high rates (kHz).
- Feature Extraction: Using FFT (Fast Fourier Transform) to convert time-domain data to frequency-domain.
- On-Board Inference: Running a lightweight TinyML model to classify motor states (Normal, Misalignment, Bearing Failure).
Example: Edge AI Implementation (C++/Arduino)
Below is a simplified code snippet showing how to process sensor data and run a pre-trained model on an edge device.
// High-Frequency Data Handling with TinyML
#include <TensorFlowLite.h>
void setup() {
Serial.begin(115200);
// Initialize High-Frequency Vibration Sensor
Sensor.begin();
}
void loop() {
float buffer[128];
// 1. Capture High-Frequency Data
for(int i=0; i<128; i++) {
buffer[i] = Sensor.readVibration();
}
// 2. Local AI Inference
String result = RunInference(buffer);
// 3. Only send critical alerts
if(result != "Normal") {
Serial.println("Warning: " + result);
SendDataToCloud(result);
}
}
Conclusion
Handling high-frequency motor data with on-board AI transforms reactive maintenance into predictive maintenance. It ensures efficiency, security, and real-time responsiveness for industrial applications.
Edge AI, TinyML, Motor Monitoring, Predictive Maintenance, IoT, High-Frequency Data, Embedded Systems
