A Neuro-Fuzzy Model for Fault Detection, Prediction and Analysis for a Petroleum Refinery

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. In: Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 3.

Abstract

Home Intelligent Systems and Applications Conference paper 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 Conference paper First Online: 07 August 2021 1324 Accesses Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 296) 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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