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 language | English |
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Title of host publication | Intelligent Systems and Applications - Proceedings of the 2021 Intelligent Systems Conference, IntelliSys |
Editors | Kohei Arai |
Publisher | Springer |
Pages | 866-876 |
Number of pages | 11 |
ISBN (Electronic) | 9783030821999 |
ISBN (Print) | 9783030821982 |
DOIs | |
Publication status | Published - 7 Aug 2021 |
Event | Intelligent Systems Conference, IntelliSys 2021 - Virtual, Online Duration: 2 Sept 2021 → 3 Sept 2021 |
Publication series
Name | Lecture Notes in Networks and Systems |
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Volume | 296 |
ISSN (Print) | 2367-3370 |
ISSN (Electronic) | 2367-3389 |
Conference
Conference | Intelligent Systems Conference, IntelliSys 2021 |
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City | Virtual, Online |
Period | 2/09/21 → 3/09/21 |
Keywords
- artificial neural network
- fault
- fuzzy
- logic
- neuron
- sensors
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Sanders, D. (Recipient), 2 Nov 0001
Prize: National/international honour