Group method of data handling and neural networks applied in temperature sensors monitoring

Bueno, Elaine Inacio; Pereira, Iraci Martinez; Silva, Antonio Teixeira e

doi:10.1504/ijnkm.2011.042003

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…the Group Method of Data Handling (GMDH) [3], Genetic Algorithms (AGs) [11] [5]. The uses of these techniques are justified because it is possible to model the process without using algebraic equations [8], by using only a database which contains the plant information.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Due to a lot of information in a short period of time, the operators are forced to take some decisions in stress conditions, so in some cases the fault diagnosis became difficult. Many techniques using Artificial Intelligence have been used in Monitoring and Fault Diagnosis with the purpose to help the nuclear power plants operators, including the Fuzzy Logic [13], Artificial Neural Networks (ANNs) [5] [1], the Group Method of Data Handling (GMDH) [3], Genetic Algorithms (AGs) [11] [5]. The uses of these techniques are justified because it is possible to model the process without using algebraic equations [8], by using only a database which contains the plant information.…”

Section: Introductionmentioning

confidence: 99%

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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“…Because of the economic losses and the operator's security, it becomes necessary to implement Monitoring and Diagnosis Systems (Maki and Loparo, 1997;Sydenham and Thorn, 1993;Clark, 1978;Echendu and Zhu, 1994). Many techniques using Artificial Intelligence have been used in Monitoring and Fault Diagnosis with the purpose to help the nuclear power plants operators, including the Fuzzy Logic (Goode, 1995), Artificial Neural Networks -ANN (Rovithakis et al, 2004;Samanta, 2004;Bueno, 2006;Bueno et al, 2010), the Group Method of Data Handling -GMDH (Bueno et al, 2011;Gonçalves, 2006), Genetic Algorithms -AG (Raymer et al, 2000;Rovithakis et al, 2004). The use of these techniques are justified because it is possible to model the process without using algebraic equations (Zupan et al, 1997), by using only a database which contains the plant information.…”

Section: Introductionmentioning

confidence: 99%

GMDH and Artificial Neural Network Applied in an Experimental Reactor Sensors Monitoring

Bueno¹,

Pereira²,

Silva³

2012

L&NLM

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In this work a Monitoring System was developed using GMDH (Group Method of Data Handling) algorithm and Artificial Neural Networks (ANN) which was applied in the IEA-R1 research reactor at IPEN. GMDH is used in two different ways to perform an input data preprocessing to the ANN. The system perform the monitoring by comparing the estimative calculated values with the measured ones. Each Monitoring System was developed and tested for five different sets of input data, ancluding data from a Reactor Teoretical Model and for four different sets of reactor variables. The results for the most of cases show an improvement when GMDH is combined with ANN algorithm in the Monitoring Systems. The good results obtained in the present work show the viability of using GMDH algorithm in the study of the best input variables to the ANN, thus making possible the use of these methods in the implementation of a new Monitoring Methodology applied in sensors of a nuclear power plant.

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Group method of data handling and neural networks applied in temperature sensors monitoring

Cited by 4 publications

References 10 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

GMDH and Artificial Neural Network Applied in an Experimental Reactor Sensors Monitoring

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