Fault detection and diagnosis using principal

International journal of Modelling, Identification and Control IJMIC
Fault detection and diagnosis using principal
component analysis. Application to low
pressure lost foam casting process.
Bendjama Hocine*
Unité de Recherche Appliquée en
Sidérurgie et Métallurgie URASM–CSC,
BP 196 Annaba, 23000, Algérie.
E mail: [email protected]
*Corresponding author
Boucherit Mohamed Elsaghir
Laboratoire de Commande des Processus. Département
de Génie Electrique. Ecole Nationale Polytechnique d'Alger
10, rue Hassen Badi, EI Harrach, Alger, Algérie - BP 182.
E mail : [email protected]
Bouhouche Salah
Unité de Recherche Appliquée en
Sidérurgie et Métallurgie URASM–CSC,
BP 196 Annaba, 23000, Algérie.
E mail: [email protected]
Bast Jürgen
HGUM, Institut für
Maschinenbau, TU
Bergakademie Freiberg,
Cotta Strasse 4, D-9596,
Germany.
E mail: bast@ imb.tufreiberg.de
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 data set by the projection
of the data set 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 data set 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 be detect and diagnose the abnormal change of the process.
Keywords: fault detection and diagnosis, principal component analysis,
multivariate statistical process control, T2-statistic, Q-statistic, squared prediction
error, Q-residual contribution.