3 research outputs found
ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing
In industry 4.0, predictive maintenance(PM) 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, the main challenges in PM 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 machines life, low accuracy computations are used when the machine is
healthy. However, on the detection of anomalies, as time progresses, the 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 to 6.65XComment: Submitted to ASP-DAC 2019, Japa
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 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.NRF (Natl Research Foundation, S’pore)Accepted versio