Abstract
This paper focuses on the development and application of a Neuro-Fuzzy (NF) networks-based scheme for Fault Detection and Isolation (FDI) in a U-tube Steam Generator (UTSG). First, a NF network is trained with data collected from a full scale UTSG simulator, and residuals are generated for fault detection. To identify the UTSG, a Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained using the Locally Linear Model Tree (LOLIMOT) algorithm which is an incremental tree structure algorithm. Then, an evolutionary algorithm is used to train a Mamdani type NF network to classify the residuals. The residuals are analyzed by using this NF classifier for fault isolation purposes.
| Original language | English |
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| Title of host publication | SAFEPROCESS'09 - 7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, Proceedings |
| Pages | 1180-1185 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 |
| Externally published | Yes |
| Event | 7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEPROCESS'09 - Barcelona, Spain Duration: 30 Jun 2009 → 3 Jul 2009 |
Conference
| Conference | 7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEPROCESS'09 |
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| Country/Territory | Spain |
| City | Barcelona |
| Period | 30/06/09 → 3/07/09 |
Keywords
- Fault Diagnosis
- Locally Linear Model Tree
- Locally Linear Neuro Fuzzy model
- Neuro-fuzzy networks
- Steam Generator
ASJC Scopus subject areas
- Control and Systems Engineering