Title: Fault detection and diagnosis using principal component analysis. Application to low pressure lost foam casting process

Authors: Hocine Bendjama, Mohamed Seghir Boucherit, Salah Bouhouche, Jurgen Bast

Addresses: Unite de Recherche Appliquee en Siderurgie et Metallurgie, URASM/CSC, BP 196, Annaba, Algerie; Laboratoire de Commande des Processus, Ecole Nationale Polytechnique, 10 Avenue Hassen Badi, BP 182, El Harrach, Alger, Algerie. ' Laboratoire de Commande des Processus, Ecole Nationale Polytechnique, 10 Avenue Hassen Badi, BP 182, El Harrach, Alger, Algerie. ' Unite de Recherche Appliquee en Siderurgie et Metallurgie, URASM/CSC, BP 196, Annaba, Algerie. ' Institut fur Maschinenbau, TU Bergakademie Freiberg, Cotta strasse 4, D-9596, Germany

Abstract: Process fault detection and diagnosis plays a very important role in the production security and the product quality. In this paper, in order to improve the accuracy for fault detection and diagnosis, a new method based on principal component analysis (PCA) is proposed in low pressure lost foam casting process. PCA method reduces the dimensionality of the original dataset by the projection of the dataset onto a smaller subspace defined by the principal components. The aim of this method is to establish the normal statistical correlation among the coefficients of the dataset to detect and diagnose the faults. The process faults are detected and diagnosed using multivariate statistical process control (MSPC) parameters such as: Hotelling|s T²-statistic, Q-statistic or squared prediction error (SPE) and Q-residual contribution. The monitoring results indicates that the proposed method can detect and diagnose the abnormal change of the process.

Keywords: process faults; fault detection; fault diagnosis; principal component analysis; PCA; T²-statistic; Q-statistic; squared prediction error; SPE; Q-residual contribution; lost foam casting.

DOI: 10.1504/IJMIC.2011.042344

International Journal of Modelling, Identification and Control, 2011 Vol.14 No.1/2, pp.102 - 111

Published online: 21 Mar 2015 *

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