Combinatorial Neural Networks Based Model for Identification of Marine Steam Turbine Clustered Parameters

Abstract

This paper presents a combinatorial model for the identification and simulation of a certain number of parameters of marine steam turbine plant for LNG tankers based on the classification and approximation neural networks. The model consists of two basic parts. In the first part, parameters are classified in adequate clusters by means of self-organizing neural network, while the combinatorial identification of clusters interrelationship is carried out in the second part by means of static feed-forward neural networks. In the following part, the successfulness of the achieved results is analyzed by generating an adequate rank-list of all identification-simulation models. This approach gives a clear insight into certain cluster interdependences which can significantly contribute in following applications which are based on the estimation and prediction of the lost sensor information not depending on the cause of their loss. Although all of the above is distinctly expressed in marine propulsion control systems, it should be pointed out that in this way significantly increased reliability and redundancy of the sensor information directly reflect on considerable increase in technical security of the whole ship as a floating object. KEY WORDS: marine steam turbines, marine control systems, neural networks, identification, clusterization