Sensor signal analysis by neural networks for surveillance in nuclear reactors

Abstract

kbstruct-The application of neural networks as a tool for toring. Signals utilized in a wear-out monitoring system reactor diagnostics is examined here. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of the degradation of a pump shaft [17] are analyzed as a semi-benchmark test to study the feasibility of neural networks for monitoring and surveillance in nuclear reactors. The Adaptive Resonance Theory (ART 2 and ART 2-A) paradigm of neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing these signals apart and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data, and provides an evaluation on the performance of ART 2-A and ART 2 for reactor signal analysis. The selection of ART 2 is due to its desired design principles such as unsupervised learning, stability-plasticity, search-direct access, and the match-reset tradeoffs. ART 2-A is selected for its speed. Two simulators are built. One is ART 2, and the other ART 2-A. The result is a success for both paradigms, and the study shows that ART 2-A is not only able to learn and distinguish the patterns from each other, its learning speed is also extremely fast despite the high-dimensional input spaces.

Image indexing and retrieval using an ART‐2A neural network architecture

“…This architecture has the following features: noise filtering, good computing and classification performance. The ART neural network family has been used in several domains, such as to recognize Chinese characters (Gan and Luan, 1992), interpretation of data from nuclear reactor sensors (Keyvan and Rabelo, 1992;Davis, 1993, 1996), image processing (Vlajic and Card, 2001), detection of earth mines (Filippidis et al, 1999), treatment of satellite images (Carpenter et al, 1997) and robot control (Bachelder et al, 1993).…”

Automatic Text Classification Using an Artificial Neural Network

“…Out of the different versions of networks ART, the architecture ART-2A may be highlighted as it allows the quick learning of the input patterns represented by continuous values. Because of its attractive features, such as noise filtering and good computing and classification performance, the neural ART network family has been used in several domains, such as to recognize Chinese characters Gan and Lua, 1992, interpretation of data originated on nuclear reactor sensors Whiteley and Davis, 1996;Whiteley and Davis, 1993;Keyvan andRabelo, 1992, image processing Vlajic andCard, 2001, detection of earth mines Filippidis et al, 1999, treatment of satellite images Carpenter et al, 1997 and robots sensorial control Bachelder et al, 1993.…”

Escalonamento de processos: uma abordagem dinâmica e incremental para a exploração de características de aplicações paralelas

“…Essas características permitem que as arquiteturas de redes ART sejam utilizadas em vários domínios de problemas, por exemplo: controle de reatores nucleares (Keyvan & Rabelo, 1992), interpretação dinâmica de sensores nucleares (Whiteley & Davis, 1996, reconhecimento de imagens de satélite (Baraldi & Panniggiani, 1995; Carpenter et al, 1997), processamento de imagens (Vlajic & Card, 2001 ), detecção de minas terrestres (Filippidis et al, 1999), controle sensorial de robôs (Bachelder et al, 1993), reconhecimento de caracteres chineses (Gan & Lua, 1992;He et al, 2002He et al, , 2003b e classificação de falhas em sistemas de controle (Benítez-Pérez & García-Nocetti, 2002). A arquitetura ART utilizada depende do tipo do problema a ser resolvido.…”