@inproceedings{965fa1ac374b415e90755894eacf9fe4,
title = "A neuro-fuzzy model for fault detection, prediction and analysis for a petroleum refinery",
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.",
keywords = "artificial neural network, fault, fuzzy, logic, neuron, sensors",
author = "Peter Omoarebun and David Sanders and Favour Ikwan and Malik Haddad and Giles Tewkesbury and Mohamed Hassan",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Intelligent Systems Conference, IntelliSys 2021 ; Conference date: 02-09-2021 Through 03-09-2021",
year = "2021",
month = aug,
day = "7",
doi = "10.1007/978-3-030-82199-9_59",
language = "English",
isbn = "9783030821982",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer",
pages = "866--876",
editor = "Kohei Arai",
booktitle = "Intelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference, IntelliSys",
}