Development of industrial process characterisation through data analysis

Aleksandar Josifovic; Jonathan Corney

doi:10.1109/SSCI.2016.7849990

Development of industrial process characterisation through data analysis

Aleksandar Josifovic, Jonathan Corney

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

Recently the world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the exploitation of large datasets for insights into machine behaviour. In the case of optimisation, where usually a simulation model is provided for the exploration of the parameter space, the new data-driven approach translates into the use of data for quantifying problems and delivering an optimal solution.

Original language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Place of Publication	Piscataway, N.J.
Publisher	IEEE Xplore
Number of pages	7
ISBN (Print)	978-1-5090-4241-8
DOIs	https://doi.org/10.1109/SSCI.2016.7849990
Publication status	Published - 13 Feb 2017

Keywords / Materials (for Non-textual outputs)

oil technology
accelerometers
data aquisition

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10.1109/SSCI.2016.7849990

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Cite this

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title = "Development of industrial process characterisation through data analysis",

abstract = "Recently the world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the exploitation of large datasets for insights into machine behaviour. In the case of optimisation, where usually a simulation model is provided for the exploration of the parameter space, the new data-driven approach translates into the use of data for quantifying problems and delivering an optimal solution.",

keywords = "oil technology, accelerometers, data aquisition",

author = "Aleksandar Josifovic and Jonathan Corney",

note = "(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.",

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AU - Corney, Jonathan

N1 - (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.

PY - 2017/2/13

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N2 - Recently the world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the exploitation of large datasets for insights into machine behaviour. In the case of optimisation, where usually a simulation model is provided for the exploration of the parameter space, the new data-driven approach translates into the use of data for quantifying problems and delivering an optimal solution.

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KW - oil technology

KW - accelerometers

KW - data aquisition

U2 - 10.1109/SSCI.2016.7849990

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M3 - Conference contribution

SN - 978-1-5090-4241-8

BT - 2016 IEEE Symposium Series on Computational Intelligence (SSCI)

PB - IEEE Xplore

CY - Piscataway, N.J.

ER -

Development of industrial process characterisation through data analysis

Abstract / Description of output

Keywords / Materials (for Non-textual outputs)

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