In industrial automation, motor signals are often corrupted by electromagnetic interference (EMI). Traditional filters like Low-Pass or Kalman filters are effective but sometimes struggle with non-linear noise. This is where Embedded AI and TinyML come into play.
Why Use AI for Noise Reduction?
Using an AI-based approach allows the system to learn the specific noise patterns of your hardware. By deploying a Deep Learning model directly onto a microcontroller, you can achieve real-time denoising without the latency of cloud computing.
The Implementation Process
- Data Collection: Collect raw sensor data (Current/Vibration) in both noisy and clean environments.
- Model Training: Use an Autoencoder or a 1D-CNN to learn the mapping from noisy to clean signals.
- Optimization: Convert the model using TensorFlow Lite Micro for embedded deployment.
Example: Implementation Code (C++/Arduino)
Below is a simplified structure of how to run a pre-trained noise reduction model on an embedded device.
#include <TensorFlowLite.h>
#include "noise_reduction_model.h" // Your exported model
// Buffer for motor signal input
float input_signal[64];
void setup() {
Serial.begin(115200);
// Initialize AI Model
tflite::InitializeModel(model_data);
}
void loop() {
// 1. Read Noisy Signal from Analog Pin
for(int i=0; i<64 2.="" 3.="" ai="" analogread="" clean_signal="" code="" delay="" denoised="" enoised="" float="" i="" inference="" input_signal="" output="" perform="" result="" serial.print="" serial.println="" the="" value:="">
Conclusion
Integrating Embedded AI for motor signal processing enhances system reliability and reduces the need for expensive hardware filters. As Edge AI continues to evolve, the ability to clean data at the source becomes a vital skill for modern engineers.
Embedded AI, TinyML, Motor Control, Noise Reduction, Signal Processing, Edge AI, Arduino, Digital Signal Processing, Predictive Maintenance
