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


  • Elaine Inacio Bueno
  • Iraci Martinez Pereira



neural networks, fuzzy logic, neuro-fuzzy, ANFIS, modeling


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.


B. SAMANTA. “Gear fault detection using Artificial Neural Networks and support vector machines with genetic algorithms”, Mechanical Systems and Signal Processing, vol. 18, n. 3, pp. 625-644, 2004.

E. I. BUENO. “Group Method of Data Handling e Redes Neurais na Monitoração e Detecção de Falhas em reatores nucleares”, Tese (Doutorado), Universidade de São Paulo-IPEN, 2011.

E. I. Bueno, I. M. Pereira, A. T. Silva. “Group method of data handling and neural networks applied in temperature sensors monitoring”, International Journal of Nuclear Knowledge Management, vol. 5, n. 3, 2011.

F. J. PELLETIER. “Review of metamathematics of fuzzy logics”, The Bulletin of Symbolic Logic, vol. 6, n. 3, pp. 342 – 346, 2000.

G. A. ROVITHAKIS, M. MANIADAKIS, M. ZERVAKIS. “A Hybrid Neural Network/Genetic Algorithm approach to optimizing feature extraction for signal validation”, IEEE Transactions on Systems, Man, And Cybernetics – Part B: CYBERNETICS, vol. 34, n. 1, pp. 695-703, 2004.

J. E. A. ECHENDU, H. ZHU. “Detecting changes in the condition of process instruments”, IEEE Transactions on Instrumentation and Measurement, vol. 43, n. 2, pp. 355-358, 1994.

J. E. TIMPERLY. “Incipient fault detection through neural RF monitoring of large rotating machines”, IEEE Trans. Power App. And Syst., vol. 102, n. 3, 1983.

J. ZUPAN, M. NOVIC, I. RUISANCHEZ. “Kohonen and counterpropagation artificial neural networks in analytical chemistry”, Chemometrics and Intelligent Laboratory Systems, vol. 38, n. 1, pp. 1-23, 1997.

L. A. ZADEH. “Fuzzy Logic”, Stanford Encyclopedia of Philosophy, Stanford University, 2006-07-23, Retrieved 2008-09-30.

L. A. ZADEH. “Fuzzy Sets”, Information and Control, vol. 8, n. 3, pp. 338-353, 1965.

M. L. RAYMER, et. al. “Dimensionality reduction using Genetic Algorithms”, IEEE Transactions on Evolutionary Computation, vol. 4, n. 2, pp. 164-171, 2000.

P. H. SYDENHAM, R. THORN. “Strategies for sensor performance assessment”, IEEE Instrumentation and Measurement Technology Conference Record, pp. 353 – 358, 1993.

P. V. GOODE. “Using a Neural/Fuzzy System to extract heuristic knowledge of incipient faults in induction motors: Part I – Methodology”, IEEE Transactions on Industrial Electronics, vol. 42, n. 2, pp. 131-138, 1995.

R. N. CLARK. “Instrument fault detection”, IEEE Transactions on Aerospace Electronic Systems, vol. 14, pp. 456-465, 1978.

S. HAYKIN. “Neural Networks – A Comprehensive Foundation”, Prentice Hall, 1999.

Y. MAKI, K. A. LOPARO. “A Neural Network approach to fault detection and diagnosis in industrial processes”, IEEE Transactions on Control System Technology, vol. 5, n. 6, pp. 529-541, 1997.

Matlab Fuzzy Logic Toolbox for use GUIs with MATLAB Version 5, The MATH WORKS Inc, MA, USA, 1997.




How to Cite

BUENO, E. I.; PEREIRA, I. M. Monitoring and fault detection: a comparative study using computational intelligence techniques. Latin American Journal of Development, [S. l.], v. 6, n. 1, p. 18–30, 2024. DOI: 10.46814/lajdv6n1-002. Disponível em: Acesso em: 3 mar. 2024.
<br data-mce-bogus="1"> <br data-mce-bogus="1">