In the era of Industrial IoT, the ability to analyze motor performance directly on the hardware—without relying on cloud or external servers—is a game changer. This approach, known as Edge Processing, reduces latency and saves bandwidth.
Why Process Signals Locally?
Processing raw motor signals (like current, voltage, or vibration) on-device allows for real-time fault detection and immediate response. By leveraging the internal DSP (Digital Signal Processing) capabilities of modern microcontrollers, we can bypass the need for external computing resources.
Key Techniques for On-Chip Processing
- Fast Fourier Transform (FFT): Converting time-domain signals into frequency-domain to identify harmonic distortions.
- Digital Filtering: Using FIR or IIR filters to remove high-frequency noise from raw ADC samples.
- RMS Calculation: Determining the effective energy consumption of the motor in real-time.
Implementation Logic
To achieve this, we utilize a circular buffer to store ADC (Analog-to-Digital Converter) values. Once the buffer is full, the MCU performs a local computation. Below is a conceptual logic flow for an embedded C environment:
// Simplified Embedded Signal Processing Logic
void ProcessMotorSignal() {
float rawSamples[1024];
float filteredOutput;
// 1. Capture Raw Signal via ADC
CaptureADC(rawSamples);
// 2. Apply Local Digital Filter (No External PC needed)
filteredOutput = ApplyLowPassFilter(rawSamples);
// 3. Analyze Frequency Components
PerformFFT(rawSamples);
// 4. Trigger Local Alarm if Threshold Exceeded
if(filteredOutput > CRITICAL_THRESHOLD) {
StopMotor();
}
}
Conclusion
By shifting the computational load to the Edge, engineers can create more resilient and autonomous motor control systems. This minimizes dependency on external infrastructure and enhances system security.
Motor Control, Embedded Systems, Signal Processing, Edge Computing, IoT, Real-time Analysis, DIY Engineering
