ADEPOS : anomaly detection based power saving for predictive maintenance using edge computing

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 f...

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Bibliographic Details
Main Authors: Bose, Sumon Kumar, Kar, Bapi, Roy, Mohendra, Gopalakrishnan, Pradeep Kumar, Basu, Arindam
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/106102
http://hdl.handle.net/10220/49166
https://doi.org/10.1145/3287624.3287716
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Institution: Nanyang Technological University
Language: English
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Summary: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.