In the era of Industry 4.0, Real-Time Motor Fault Diagnosis Using On-Board AI has become a game-changer for predictive maintenance. Instead of sending raw data to the cloud, we can now process vibration and thermal data directly on the motor using TinyML.
Why Use On-Board AI for Motor Diagnostics?
Traditional monitoring systems often suffer from latency and high bandwidth costs. By implementing On-Board AI, we achieve:
- Instant Detection: Identifying bearing failures or misalignments in milliseconds.
- Reduced Data Traffic: Only anomaly alerts are sent to the central server.
- Enhanced Reliability: The system works even without a constant internet connection.
The Workflow: From Sensors to Insight
To perform effective motor fault diagnosis, the On-Board AI follows a specific pipeline:
- Data Acquisition: Collecting high-frequency data from accelerometers and current sensors.
- Feature Extraction: Using Fast Fourier Transform (FFT) to convert time-domain signals into frequency-domain features.
- Inference: A pre-trained neural network (running on an MCU) classifies the motor state (Normal, Unbalance, Bearing Fault).
Sample Edge AI Logic (Pseudo-code)
Below is a conceptual look at how an On-Board AI script handles real-time inference:
// Real-time Inference Loop
void loop() {
float vibrationData = readAccelerometer();
// Run On-Board AI Model
int faultType = motorModel.predict(vibrationData);
if(faultType != NORMAL_STATE) {
triggerEmergencyStop();
sendAlertToDashboard(faultType);
}
}
Conclusion
Implementing Real-Time Motor Fault Diagnosis using TinyML and On-Board AI ensures your machinery stays healthy while optimizing operational costs. As Edge AI hardware evolves, the transition from reactive to proactive maintenance has never been easier.
AI, TinyML, Motor Diagnosis, Predictive Maintenance, Edge AI, IoT, Engineering
