Group Method of Data Handling (GMDH) e redes neurais na monitoração e detecção de falhas em sensores de centrais nucleares

Bueno, Elaine Inacio

doi:10.11606/t.85.2011.tde-02092011-140535

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…These methodologies was developed and tested using a model composed by 9 variables: N2, T3, T4, T7, T8, T9, F1M3, F2M3 and R1M3, which were described in section V. This model was used previously in a Monitoring System [2].…”

Section: Monitoring and Fault Detection Systemmentioning

confidence: 99%

Monitoring and fault detection: a comparative study using computational intelligence techniques

Bueno,

Pereira

2024

Lat. Americ. J. of Develop.

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In this work, two different Computational Intelligence Techniques were used to provide a comparative study by using Neural Networks and Neuro-fuzzy applied in Monitoring and Fault Detection in sensor of an experimental reactor. Both methodologies were developed and tested using a model composed by 9 variables: N2 (% power), T3 (pool water temperature), T4 (decay tank inlet temperature), T7 (primary loop outlet temperature), T8 (secondary loop inlet temperature), T9 (secondary loop outlet temperature), F1M3 (primary loop flowrate), F2M3 (secondary loop flow rate) and R1M3 (nuclear dose rate). It was used data from the first week experimental reactor IEA-R1 operation from October 2012 in both Monitoring Systems, which was divided in subsets in a following way: 60% for training, 20% for tens and 20% for validation. The results obtained using Neuro-fuzzy were better than the ones with Neural Networks.

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Section: Monitoring and Fault Detection Systemmentioning

confidence: 99%

Monitoring and fault detection: a comparative study using computational intelligence techniques

Bueno,

Pereira

2024

Lat. Americ. J. of Develop.

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Monitoring and fault detection: a comparative study using computational intelligence techniques

Pereira¹,

Bueno²

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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Abstract-In this work, two different computational intelligence techniques -Neural Networks and Neuro-fuzzywere used in the development of a Monitoring and Fault Detection system applied to the IEA-R1 experimental nuclear reactor. The monitoring is made by comparing an estimative of the variable generated by the computational intelligence techniques with the actual value. Both techniques have been successfully applied to the Monitoring System and were effective in estimate a monitored variable. The Neuro-Fuzzy technique showed strongly better performance compared with Neural Network.

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Group Method of Data Handling (GMDH) e redes neurais na monitoração e detecção de falhas em sensores de centrais nucleares

Cited by 2 publications

References 27 publications

Monitoring and fault detection: a comparative study using computational intelligence techniques

Monitoring and fault detection: a comparative study using computational intelligence techniques

Monitoring and fault detection: a comparative study using computational intelligence techniques

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