Hybrid Fault Detection Method for a Distillation Unit
Fault detection and isolation have become increasingly important problems over time, due to the more complex and larger scale industrial systems. The last few decades have seen a rise in research focused on developing robust and sensitive fault detection methods. Approaches using mathematical models...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
AIDIC Servizi S.r.l.
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/57b85c14fd9946fc8546d6300e4e0803 |
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| Sumario: | Fault detection and isolation have become increasingly important problems over time, due to the more complex and larger scale industrial systems. The last few decades have seen a rise in research focused on developing robust and sensitive fault detection methods. Approaches using mathematical models, qualitative logic or operation data driven solutions were developed and while they all performed well overall some lacked in robustness and others in flexibility or sensitivity. In this study a hybrid fault detection approach using both parity relation methods and a Fuzzy Expert System (FES) to analyse and detect process faults in a distillation unit is introduced. The combination of the two schemes was used to handle the detection and classification of additive and multiplicative faults. The effectiveness of the hybrid method in alleviating the shortcomings of the single techniques has been verified by simulation and experimental tests. |
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