Mastering Predictive Maintenance with TinyML and Real-time Adaptation.
Introduction
In the era of Industry 4.0, Motor Diagnostics has evolved from simple threshold alerts to sophisticated Edge AI models. However, static models often fail when operating conditions change. This guide explores how to implement Self-Adaptive Motor Diagnostics on Edge boards (like Arduino Pro Nicla, ESP32, or Raspberry Pi) to ensure high accuracy in dynamic environments.
Why "Self-Adaptive" Matters
Traditional diagnostic models are trained on specific datasets. If a motor’s load changes or mechanical wear occurs naturally, the model might trigger false positives. A self-adaptive system continuously updates its baseline or uses On-device Learning to adjust its parameters without needing a complete firmware re-flash.
Core Implementation Logic
The following conceptual C++ snippet demonstrates how an Edge AI board can adapt its anomaly detection threshold based on the running average of vibration data.
// Conceptual Code for Self-Adaptive Thresholding
float vibrationValue = 0.0;
float dynamicThreshold = 1.5; // Initial baseline
float alpha = 0.01; // Learning rate for adaptation
void loop() {
vibrationValue = readVibrationSensor(); // Get data from IMU/Accelerometer
// Check for anomalies
if (vibrationValue > dynamicThreshold * 2.0) {
triggerAlert("Potential Bearing Failure Detected!");
} else {
// Self-Adaptation Logic: Slowly update the baseline
// to account for normal operational drift
dynamicThreshold = (alpha * vibrationValue) + ((1 - alpha) * dynamicThreshold);
}
delay(100);
}
Steps to Deploy on Edge Boards
- Data Acquisition: Collect high-frequency vibration and current data using sensors like the MPU6050.
- Feature Engineering: Extract FFT (Fast Fourier Transform) features to identify frequency peaks.
- Model Optimization: Convert your TensorFlow or PyTorch model to TensorFlow Lite Micro.
- Edge Deployment: Flash the model and implement the self-adaptive feedback loop.
