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Title: Live demonstration : autoencoder-based predictive maintenance for IoT
Authors: Gopalakrishnan, Pradeep Kumar
Kar, Bapi
Bose, Sumon Kumar
Roy, Mohendra
Basu, Arindam
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Gopalakrishnan, P. K., Kar, B., Bose, S. K., Roy, M., & Basu, A. (2019). Live demonstration : autoencoder-based predictive maintenance for IoT. Proceedings of the 2019 IEEE International Symposium on Circuits and Systems (ISCAS). doi:10.1109/iscas.2019.8702230
Conference: 2019 IEEE International Symposium on Circuits and Systems (ISCAS)
Abstract: This live demo aims to show the performance of a two-layer neural network applied to predictive maintenance. The first layer encodes features based on prior knowledge, while the second layer is trained online to detect anomalies. The system is implemented on an FPGA, acquiring real-time data from sensors attached to a motor. Faults can be triggered artificially in real-time to demonstrate anomaly detection.
ISBN: 9781728103976
DOI: 10.1109/ISCAS.2019.8702230
Schools: School of Electrical and Electronic Engineering 
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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