Real-time Deep Neural Networks for internet-enabled arc-fault detection

We examine methods for detecting and disrupting electronic arc faults , proposing an approach leveraging Internet of Things connectivity, artificial intelligence, and adaptive learning. We develop Deep Neural Networks (DNNs) taking Fourier coefficients , Mel-Frequency Cepstrum data, and Wavelet features as input for differentiating normal from malignant current measurements . We further discuss how hardware-accelerated signal capture facilitates real-time classification, enabling our classifier to reach 99.95% accuracy for binary classification and 95.61% for multi-device classification, with trigger-to-trip latency under 220 ms . Finally, we discuss how IoT supports aggregate and user-specific risk models and suggest how future versions of this system might effectively supervise multiple circuits.