A neuro-fuzzy model for fault detection, prediction and analysis for a petroleum refinery
A neuro-fuzzy model for fault detection, prediction and analysis for a petroleum refinery
The paper describes data fusion using a neuro-fuzzy system for fault detection, prediction, and analysis of petroleum refining operations and other process industries. The model described in this paper involves algorithms applied to multi-sensor fusion using historical data to create a trend analysis. The main objective is to detect anomalies in sensor data and to predict future catastrophes. Data mining is applied to find anomalies in data sets. Neuro-fuzzy logic is used to find clusters of inputs using subtractive fuzzy clustering. Fault detection and prognosis are essential in a safety-critical environment such as a refinery. A new set of data is obtained and represented using the fuzzy inference system, with three linguistic values used to define and classify the patterns and failures.
Fuzzy Logic Fault Sensors Neuron Artificial Neural Network
866-876
Omoarebun, Peter
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Sanders, David
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Ikwan, Favour
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Haddad, Malik
cdc55972-df6f-492d-8ed0-b022e19b912f
Tewkesbury, Giles
f569295c-fb95-4288-a6bc-7d9e5af15b8d
Hassan, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Omoarebun, Peter
d2bac592-82c7-450c-830c-49a7334fda1e
Sanders, David
c4132517-7bde-45d6-adaf-6859f42cec8e
Ikwan, Favour
504e862a-ecff-41ce-85ba-38d95e34aad1
Haddad, Malik
cdc55972-df6f-492d-8ed0-b022e19b912f
Tewkesbury, Giles
f569295c-fb95-4288-a6bc-7d9e5af15b8d
Hassan, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Omoarebun, Peter, Sanders, David, Ikwan, Favour, Haddad, Malik, Tewkesbury, Giles and Hassan, Mohamed
(2021)
A neuro-fuzzy model for fault detection, prediction and analysis for a petroleum refinery.
Arai, Kohei
(ed.)
In Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys).
vol. 3,
Springer Cham.
.
(doi:10.1007/978-3-030-82199-9_59).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The paper describes data fusion using a neuro-fuzzy system for fault detection, prediction, and analysis of petroleum refining operations and other process industries. The model described in this paper involves algorithms applied to multi-sensor fusion using historical data to create a trend analysis. The main objective is to detect anomalies in sensor data and to predict future catastrophes. Data mining is applied to find anomalies in data sets. Neuro-fuzzy logic is used to find clusters of inputs using subtractive fuzzy clustering. Fault detection and prognosis are essential in a safety-critical environment such as a refinery. A new set of data is obtained and represented using the fuzzy inference system, with three linguistic values used to define and classify the patterns and failures.
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More information
e-pub ahead of print date: 6 August 2021
Keywords:
Fuzzy Logic Fault Sensors Neuron Artificial Neural Network
Identifiers
Local EPrints ID: 484352
URI: http://eprints.soton.ac.uk/id/eprint/484352
ISSN: 2367-3370
PURE UUID: 48413df2-92a9-48a9-883e-af3ef88eff11
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Date deposited: 15 Nov 2023 18:23
Last modified: 18 Mar 2024 03:55
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Contributors
Author:
Peter Omoarebun
Author:
David Sanders
Author:
Favour Ikwan
Author:
Malik Haddad
Author:
Giles Tewkesbury
Editor:
Kohei Arai
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