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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Omoarebun, P., Sanders, D., Ikwan, F., Haddad, M., Tewkesbury, G., Hassan, M. (2022). A Neuro-Fuzzy Model for Fault Detection, Prediction and Analysis for a Petroleum Refinery. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_59
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