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Condition Monitoring of Casting Process using Multivariate Statistical Method

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Academic year: 2021

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Condition Monitoring of Casting Process using Multivariate Statistical Method

Hocine Bendjama, Kaddour Gherfi, Daoud Idiou

Welding and NDT Research Centre (CSC)

Algiers, Algeria

e-mails: hocine_bendjama@daad-alumni.de, gkaddour2@yahoo.fr, ddidiou@yahoo.com

Jürgen Bast

Institut für Maschinenbau Technische Universität Bergakademie

Freiberg, Germany e-mail: bast@imb.tu-freiberg.de

Abstract—Growing demand for higher performance, safety and reliability of industrial systems has increased the need for condition monitoring and fault diagnosis. A wide variety of techniques were used for process monitoring. This study will mainly investigate a technique based on principal component analysis in order to improve the accuracy for fault diagnosis of casting process. The process faults are identified using the following statistical parameters: Q-statistic, also called squared prediction error, and Q-residual contribution. The proposed method is evaluated using real sensor measurements from a pilot scale. The monitoring results indicate that the principal component analysis method can diagnose the abnormal change in the measured data.

Keywords-fault diagnosis, process monitoring, principal component analysis, Q-statistic, Q- residual contribution

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