Detection and Diagnosis of Faults in a Four-Tank System using Artificial Neural Networks

Eduardo Apaza Alvarez, Elvis J. Alegria

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This paper proposes an artificial neural network-based method for fault detection and diagnosis of a MIMO four-tank process in a non-minimum phase. The approach considers two stages: a model output estimation error stage, where a nonlinear autoregressive exogenous neural network is used to model the system, and a fault detection and diagnosis stage based on the model output error estimates, where a standard feed-forward neural network is used to classify the kind of fault. Faults due to added noise and parametric changes are combined as a benchmark to be detected in real-time to evaluate this proposal. Therefore, the whole system considers two setpoint inputs, four transfer functions, two NARX neural networks, four feed-forward pattern recognition networks, and four outputs, each associated with a specific fault.

Idioma originalInglés
Título de la publicación alojada2022 IEEE ANDESCON
Subtítulo de la publicación alojadaTechnology and Innovation for Andean Industry, ANDESCON 2022
EditoresMariela Cerrada Lozada, Paul Sanmartiin Mendoza
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665488549
DOI
EstadoPublicada - 2022
Evento11th IEEE Conference of the Andean Council, ANDESCON 2022 - Barranquilla, Colombia
Duración: 16 nov. 202219 nov. 2022

Serie de la publicación

Nombre2022 IEEE ANDESCON: Technology and Innovation for Andean Industry, ANDESCON 2022

Conferencia

Conferencia11th IEEE Conference of the Andean Council, ANDESCON 2022
País/TerritorioColombia
CiudadBarranquilla
Período16/11/2219/11/22

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