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Event Detection and Predictive Maintenance using Component Echo State Networks

Westholm, Jonatan (2018) FMS820 20181
Mathematical Statistics
Abstract
With a growing number of sensors collecting information about systems in indus-
try and infrastructure, one wants to extract useful information from this data.
The goal of this project is to investigate the applicability of Echo State Net-
work techniques to time-varying classification of multivariate time series from
primarily mechanical and electrical systems. Two relevant technical problems
are predicting impending failure of systems (predictive maintenance), and clas-
sifying a common event related to the system (event detection). In this project,
they are formulated as a supervised machine learning problem on a multivariate
time series. For this problem, Echo State Networks (ESN) have proven effective.
However, applying these... (More)
With a growing number of sensors collecting information about systems in indus-
try and infrastructure, one wants to extract useful information from this data.
The goal of this project is to investigate the applicability of Echo State Net-
work techniques to time-varying classification of multivariate time series from
primarily mechanical and electrical systems. Two relevant technical problems
are predicting impending failure of systems (predictive maintenance), and clas-
sifying a common event related to the system (event detection). In this project,
they are formulated as a supervised machine learning problem on a multivariate
time series. For this problem, Echo State Networks (ESN) have proven effective.
However, applying these algorithms to new data sets involves a lot of guesswork
as to how the algorithm should be configured to model the data effectively. In
this work, a modification of the Echo State Network (ESN) model is presented,
that helps to remove some of this guesswork. The new algorithm uses specifically
structured components in order to facilitate the generation of relevant features
by the ESN. The algorithm is tested on two easy event detection data sets, and
one hard predictive maintenance data set. The results are compared to Support
Vector Machine and Multilayer Perceptron classifiers, as well as to a basic ESN,
which is also implemented as a reference. The component ESN successfully
generates promising features, and outperforms the minimum complexity ESN
as well as the standard classifiers. (Less)
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author
Westholm, Jonatan
supervisor
organization
course
FMS820 20181
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
8931445
date added to LUP
2018-01-08 10:44:13
date last changed
2018-01-08 10:44:13
@misc{8931445,
  abstract     = {With a growing number of sensors collecting information about systems in indus-
try and infrastructure, one wants to extract useful information from this data.
The goal of this project is to investigate the applicability of Echo State Net-
work techniques to time-varying classification of multivariate time series from
primarily mechanical and electrical systems. Two relevant technical problems
are predicting impending failure of systems (predictive maintenance), and clas-
sifying a common event related to the system (event detection). In this project,
they are formulated as a supervised machine learning problem on a multivariate
time series. For this problem, Echo State Networks (ESN) have proven effective.
However, applying these algorithms to new data sets involves a lot of guesswork
as to how the algorithm should be configured to model the data effectively. In
this work, a modification of the Echo State Network (ESN) model is presented,
that helps to remove some of this guesswork. The new algorithm uses specifically
structured components in order to facilitate the generation of relevant features
by the ESN. The algorithm is tested on two easy event detection data sets, and
one hard predictive maintenance data set. The results are compared to Support
Vector Machine and Multilayer Perceptron classifiers, as well as to a basic ESN,
which is also implemented as a reference. The component ESN successfully
generates promising features, and outperforms the minimum complexity ESN
as well as the standard classifiers.},
  author       = {Westholm, Jonatan},
  language     = {eng},
  note         = {Student Paper},
  title        = {Event Detection and Predictive Maintenance using Component Echo State Networks},
  year         = {2018},
}