Fault detection and diagnosis using principal component analysis. Application to low pressure lost foam casting process Online publication date: Sat, 21-Mar-2015
by Hocine Bendjama, Mohamed Seghir Boucherit, Salah Bouhouche, Jurgen Bast
International Journal of Modelling, Identification and Control (IJMIC), Vol. 14, No. 1/2, 2011
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.
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