In the era of Industry 4.0, Predictive Maintenance has become essential for reducing downtime. This article explores how to implement TinyML techniques for Motor Fault Detection on Edge Devices, allowing for real-time monitoring without relying on cloud connectivity.
Why TinyML for Motor Analysis?
Traditional vibration analysis requires high bandwidth to send raw data to the cloud. By using TinyML (Tiny Machine Learning), we can process accelerometer data directly on microcontrollers like Arduino Nano 33 BLE Sense or ESP32. This reduces latency and enhances data privacy.
The Workflow: From Vibration to Insight
- Data Collection: Capturing 3-axis vibration data (X, Y, Z) from healthy and faulty motors.
- Signal Processing: Applying Fast Fourier Transform (FFT) to convert time-domain data into frequency-domain features.
- Model Training: Using platforms like Edge Impulse to train a Neural Network (ANN) or Random Forest.
- Deployment: Exporting the model as a C++ library to run on Edge Devices.
Sample Code: Preprocessing on Edge Device
Below is a snippet for capturing and scaling sensor data before feeding it into the inference engine:
#include <TinyML_Motor_Inferencing.h>
void loop() {
// Get raw accelerometer data
float x, y, z;
if (IMU.accelerationAvailable()) {
IMU.readAcceleration(x, y, z);
}
// Input buffer for the TinyML model
float input_buffer[3] = {x, y, z};
// Run Inference
ei_impulse_result_t result = { 0 };
EI_IMPULSE_ERROR res = run_classifier(&signal, &result, false);
if (result.classification[1].value > 0.8) {
Serial.println("Warning: Motor Fault Detected!");
}
}
Key Benefits
- Low Power Consumption: Ideal for battery-powered sensors.
- Real-time Response: Instant detection of bearing wear or misalignment.
- Cost-Effective: Eliminates expensive cloud storage fees.
Integrating Edge AI into motor systems is the future of smart manufacturing. By leveraging TinyML, engineers can build smarter, more resilient infrastructure.
TinyML, Edge AI, Motor Fault Detection, Predictive Maintenance, Arduino, ESP32, Machine Learning, IoT, Industrial IoT
