A neuro-fuzzy model for fault detection, prediction and analysis for a petroleum refinery

Peter Omoarebun*, David Sanders, Favour Ikwan, Malik Haddad, Giles Tewkesbury, Mohamed Hassan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference, IntelliSys
EditorsKohei Arai
PublisherSpringer
Pages866-876
Number of pages11
ISBN (Electronic)9783030821999
ISBN (Print)9783030821982
DOIs
Publication statusPublished - 7 Aug 2021
Event Intelligent Systems Conference, IntelliSys 2021 - Virtual, Online
Duration: 2 Sep 20213 Sep 2021

Publication series

NameLecture Notes in Networks and Systems
Volume296
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference Intelligent Systems Conference, IntelliSys 2021
CityVirtual, Online
Period2/09/213/09/21

Keywords

  • artificial neural network
  • fault
  • fuzzy
  • logic
  • neuron
  • sensors

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