13 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
Application of classification methods in fault detection and diagnosis of inverter fed induction machine drive: a trend towards reliability
The aim of this paper is to present a
method of detection and isolation of intermittent misfiring in power
switches of a three phase inverter feeding an induction machine drive. The
detection and diagnosis procedure is based solely on the output currents of
the inverter flowing into the machine windings. The measured currents are
transformed in the two dimensional frame obtained with the Concordia
transform. The data are then treated by a time-average method. The results
even promising lack of accuracy mainly in the fault isolation step.
To enhance the fault detection and diagnosis by the use of the
information enclosed in the data, a Principal Component Analysis classifier
is applied. The detection of a fault occurrence is made by a two-class
classifier. The isolation is a two-step approach which uses the Linear
Discriminant Analysis; the first is to identify the faulty leg with a
three-class classifier and the second one discriminates the faulty power
switch. Both methods are evaluated with experimental data and pattern
recognition method proves its effectiveness and accuracy in the faulty leg detection and isolation
Special Section on Condition Monitoring and Fault Accommodation in Electric and Hybrid Propulsion Systems
Application of classification methods in fault detection and diagnosis of inverter fed induction machine drive: a trend towards reliability
A Comparison of Symmetrical and Asymmetrical Three-Phase H-Bridge Multilevel Inverter for DTC Induction Motor Drives
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 machineâs
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, Sâpore)Accepted versio
Guest Editorial Energy Conversion in Next-generation Electric Ships
The papers in this special section focus on energy conversion in next generation electric ships. These papers bring together papers focused on the recent advancements in energy conversion techniques used in electric ship technologies