Inferential Sensor - Based Adaptive Principal Components Analysis of Mould BathLevel for Breakout Defect Detection and Evaluation in Continuous Casting

Type : Publication
Auteur(s) :  Salah Bouhouche, Zoheir MENTOURI, Slimane ZIANI, Bast Jurgen
Année :  2015
Domaine : Electronique
Revue : Applied Soft Computing
Résumé en PDF :  (résumé en pdf)
Fulltext en PDF :  (.pdf)
Mots clés :  Soft sensor, continuous casting, Adaptive principal component analysis, breakout detection and evaluation.

Résumé : 

This paper is concerned with a method for breakout defect detection and evaluation in a continuouscasting process. This method uses Adaptive Principal Component Analysis (APCA) as a predictor of inputs -outputs model, which are defined by the mould bath level and casting speed. The main difficulties that causebreakout in continuous casting are, generally, phenomenon related to the non-linear and unsteady state of themetal solidification process. PCA is a modeling method based on linear projection of the principal components;the adaptive version developed in this work uses the sliding window technique for the estimation of the modelparameters. This recursive form updates the new model parameters; it gives a reliable and accurate prediction.Simulation results compare PCA, APCA, nonlinear system identification using neural network (NN) and supportvector regression (SVR) methods showing that the APCA gives the best Mean Squared Error (MSE). Based onthe MSE, the proposed approach is analyzed, tested and improved to give an accurate breakout detection andevaluation system.