Estimation of the Insulation Deterioration of Metallurgical Ladle by Use of RBFNN Models

A model-based approach for estimation and diagnosis of the deterioration in the metallurgical ladle insulation is proposed in this paper. It is based on using the diverse information that comes from the so called thermo vision analysis (thermographic images), which show the temperature profile on the surface of the ladle. A group of Radial Basis Function Neural Network (RBFNN) models with different structures is developed and used for such estimation. Each model has different number of input parameters and a different output, in order to estimate the respective parameters of the insulation deterioration (the defect), such as its depth, width and shape. The created RBFNN models are a kind of diagnostic models because they solve the inverse problem, namely: finding the parameters of the defect, taking into account the available measured symptoms (the selected parameters from the thermographic images). The estimation results from all proposed diagnostic models are shown and discussed in the paper, by using simulated input/output data sets. Respective suggestions and procedure for selection of the best diagnostic model are also given in the paper.

I. Introduction

Nondestructive monitoring and estimation of different kinds of deterioration in the metallurgical equipment is very import for achieving smooth and faultless operation of the metallurgical processes [2], [5], [6].