Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106102
Title: ADEPOS : anomaly detection based power saving for predictive maintenance using edge computing
Authors: Bose, Sumon Kumar
Kar, Bapi
Roy, Mohendra
Gopalakrishnan, Pradeep Kumar
Basu, Arindam
Keywords: Anomaly Detection
Approximate Computing
Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Bose, S. K., Kar, B., Roy, M., Gopalakrishnan, P. K., & Basu, A. (2019). ADEPOS : anomaly detection based power saving for predictive maintenance using edge computing. 2019 24th Asia and South Pacific Design Automation Conference. doi:10.1145/3287624.3287716
Conference: 2019 24th Asia and South Pacific Design Automation Conference
Abstract: In Industry 4.0, predictive maintenance (PdM) is one of the most important applications pertaining to the Internet of Things (IoT). Machine learning is used to predict the possible failure of a machine before the actual event occurs. However, main challenges in PdM are: (a) lack of enough data from failing machines, and (b) paucity of power and bandwidth to transmit sensor data to cloud throughout the lifetime of the machine. Alternatively, edge computing approaches reduce data transmission and consume low energy. In this paper, we propose Anomaly Detection based Power Saving (ADEPOS) scheme using approximate computing through the lifetime of the machine. In the beginning of the machine’s life, low accuracy computations are used when machine is healthy. However, on detection of anomalies as time progresses, system is switched to higher accuracy modes. We show using the NASA bearing dataset that using ADEPOS, we need 8.8X less neurons on average and based on post-layout results, the resultant energy savings are 6.4-6.65X.
URI: https://hdl.handle.net/10356/106102
http://hdl.handle.net/10220/49166
DOI: 10.1145/3287624.3287716
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: https://doi.org/10.1145/3287624.3287716
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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