EDGE PULSE

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  “System overview of EdgePulse: from power signal acquisition to AI-based fault detection at the edge.”

Power is the backbone of modern life, yet power quality issues like voltage fluctuations, harmonics, and unexpected faults continue to cause equipment damage, energy loss, and costly downtime across homes, industries, and critical infrastructure. Most existing monitoring systems rely heavily on cloud processing, which introduces latency, higher costs, and dependency on stable internet connectivity—making them unsuitable for time-critical power fault detection.

Edge Pulse is an AI-powered, edge-based power quality and fault detection system designed to analyze electrical signals in real time, directly at the source. Instead of sending raw data to the cloud, EdgePulse processes voltage and current signals locally using signal processing techniques (FFT) and machine learning models to identify anomalies, harmonics, and early signs of faults.

Faster fault detection. Reduced energy losses. Predictive maintenance. Improved system reliability.

Built With

• ai
• ai-models
• and-numerical-computations-scikit-learn-?-machine-learning-models-for-fault-and-anomaly-detection-matplotlib-/-plotly-?-visualization-of-voltage
• and-system-logic-numpy-&-scipy-?-power-signal-analysis
• dataset
• fft
• github
• matplotlib
• ml
• numpy
• power
• python
• singal
• stimulated