Real-time Deep Neural Networks for internet-enabled arc-fault detection. (September 2018)

• Record Type: Journal Article
• Authors: Siegel, Joshua E.; Pratt, Shane; Sun, Yongbin; Sarma, Sanjay E.
• Is Part Of: Engineering applications of artificial intelligence. Volume 74(2018)
• Page Start: 35
• Page End: 42
• Publication Date: 2018-09
• Subjects: Emerging applications and technology -- Intelligent infrastructure -- Ambient intelligence -- Embedded intelligence -- Distributed sensing -- Arc fault detection -- Real-time; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.05.009
• Languages: English
• ISSNs: 0952-1976

Abstract

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 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.