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Ramadevi Rathinasabapathy et al./ Indian Journal of Computer Science and Engineering (IJCSE)







Head, Department of Electronics & Instrumentation, Sathyabama University, Jeppiaar Nagar, Rajiv Gandhi Road, Chennai 600 119, Tamilnadu, India



Vice Chancellor, Sathyabama University, Jeppiaar Nagar, Rajiv Gandhi Road, Chennai 600 119, Tamilnadu, India


Head, Condition Monitoring Section, FRTG, Indira Gandhi Centre for Atomic Research, Kalpakkam, Chennai 603 102, Tamilnadu, India


Former Director, FRTG, Indira Gandhi Centre for Atomic Research, Kalpakkam, Chennai 603 102, Tamilnadu, India



This paper aims to analyze the quality of pressure drop devices (orifices), which is used for flow zoning in proto type fast breeder reactor (PFBR) by analyzing the occurrence of cavitation. The magnitude of root mean square (RMS) of the acoustic time signal acquired from an accelerometer installed downstream of the cavitation test section. In this paper, the statistical feature based on the first-order distribution measure kurtosis (dimensionless parameter) is selected as input feature. Depends on mean and standard deviation values kurtosis varied i.e. peakness of the distribution varied. Nevertheless, the presence of background noise can have an influence on the values of these cavitation indicators. An adequately trained neural network is used for classification of a pressure drop devices as cavitating or non cavitating, under given operating conditions. Neural network used here is Elman Recurrent Networks which propagate data from later processing stage to earlier stage. A copy of the previous values of the hidden units is maintained which allows the network to perform sequence-prediction. The training algorithm used is the resilient back propagation algorithm. It is a systematic method to train the neural network. The purpose of it is to eliminate the harmful effects of the magnitudes of the partial derivatives. Only the sign of the derivative is used to determine the direction of the weight update and the magnitude of the derivative has no effect on the weight update. The proposed recurrent network contains 5 layers. The extracted feature (kurtosis) is normalized between –1 to +1 and fed as input to ANN model. The classification range has been fixed, from kurtosis values of varies cavitation signals such as no cavitation, incipient cavitation and developed cavitation signals. It is concluded that the performance error of the recurrent network is – 0.0093.

Key words: Kurtosis, ANN model, Recurrent Network, Resilient BPN Algorithm. I. Introduction

To regulate flow in proportion to the heat generated in the subassembly of PFBR the reactor core has been divided into 15 flow zones. This is achieved by installing pressure drop devices like orifice at the foot of the subassembly [1]. These devices should meet the pressure drop requirements without any cavitation. The cavitation free performance of the device must be ensured by detection of the various cavitation stages such as no cavitation, incipient and developed cavitation.

ISSN : 0976-5166 Vol. 2 No. 3 Jun-Jul 2011 411

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