Interpretations of fault identification in multivariate manufacturing processes Online publication date: Wed, 13-May-2015
by Kyu Jong Lee; Ji Hoon Kang; Jae Hong Yu; Seoung Bum Kim
European J. of Industrial Engineering (EJIE), Vol. 9, No. 3, 2015
Abstract: Multivariate control charts have been widely recognised as efficient tools for detection of abnormal behaviour in multivariate processes. However, these charts provide only limited information about the contribution of any specific variable to an out-of-control signal. To address this limitation, some fault identification methods have been developed to identify contributors to an abnormality. In real situations, however, a couple of tasks should be further considered with these contributors to improve their applicability and to facilitate interpretation of faults. This study presents a rank sum-based summarisation technique and a decision tree algorithm to facilitate the interpretation of fault identification results. Experimental results with real data from the manufacturing process for a thin-film transistor-liquid crystal display (TF-LCD) demonstrate the applicability and effectiveness of the proposed methods. [Received 11 February 2013; Revised 9 August 2013, 17 February 2014; Accepted 15 May 2014]
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