In the era of Industry 4.0, waiting for a motor to fail is no longer an option. Continuous Fault Scoring on edge devices allows engineers to detect anomalies in real-time, reducing downtime and maintenance costs. This guide explores how to implement an efficient fault scoring system directly on the hardware.
Why Edge Devices for Motor Monitoring?
Processing data at the source (on the edge) offers low latency, enhanced security, and significantly reduced bandwidth usage. Instead of sending raw vibration data to the cloud, we process it locally to generate a "Health Score."
Key Components of the System
- Data Acquisition: High-frequency vibration sensors (accelerometers).
- Feature Extraction: Converting raw signals into the frequency domain using FFT.
- Anomaly Detection: Utilizing TinyML models like Autoencoders or Isolation Forests.
Step-by-Step Implementation
1. Signal Processing with FFT
To identify faults like bearing wear or misalignment, we must analyze the frequency spectrum. Here is a simplified logic for calculating a fault score based on energy deviation:
// Simplified Pseudo-code for Edge Fault Scoring
float calculateFaultScore(float* vibrationData, int size) {
float fftResult = performFFT(vibrationData);
float baselineEnergy = 0.50; // Pre-calculated healthy baseline
// Calculate current energy deviation
float currentEnergy = calculateEnergy(fftResult);
float faultScore = (currentEnergy - baselineEnergy) / baselineEnergy;
return constrain(faultScore, 0.0, 1.0);
}
2. Real-time Scoring Logic
The Continuous Fault Scoring algorithm compares real-time input against a learned baseline. If the score exceeds a predefined threshold (e.g., 0.7), an alert is triggered immediately on the device.
Conclusion
Implementing continuous fault scoring on edge devices transforms reactive maintenance into a proactive strategy. By leveraging TinyML and Edge Computing, industries can ensure motor longevity and operational efficiency.
