Real-time Deep Neural Networks for internet-enabled arc-fault detection
Author(s)
Siegel, Joshua E
Pratt, Shane Richard
Sun, Yongbin
Sarma, Sanjay E
Date Issued
June 2018
Journal
Engineering Applications of Artificial Intelligence
Publisher
Elsevier
Citation
Siegel, Joshua E. et al. "Real-time Deep Neural Networks for internet-enabled arc-fault detection." Engineering Applications of Artificial Intelligence 74 (September 2018): 35-42 © 2018 Elsevier
Version
Original manuscript
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 200 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. Keywords: Emerging applications and technology; Intelligent infrastructure; Ambient intelligence; Embedded intelligence; Distributed sensing; Arc fault detection; Real-time
MIT Department
Massachusetts Institute of Technology. Office of Digital Learning
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.engappai.2018.05.009
