Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6854123 | Engineering Applications of Artificial Intelligence | 2018 | 8 Pages |
Abstract
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 220ms. 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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Joshua E. Siegel, Shane Pratt, Yongbin Sun, Sanjay E. Sarma,