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 Sept 20213 Sept 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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