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Mathematical Problems in Engineering
Volume 2013, Article ID 516760, 7 pages
Research Article

Fault Identification in Industrial Processes Using an Integrated Approach of Neural Network and Analysis of Variance

Department of Statistics and Information Science, Fu Jen Catholic University, 510 Chungcheng Road, Xinzhuang District, New Taipei City 24205, Taiwan

Received 21 November 2012; Revised 28 April 2013; Accepted 14 May 2013

Academic Editor: Jun Zhao

Copyright © 2013 Yuehjen E. Shao and Chia-Ding Hou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Due to its importance in process improvement, the issue of determining exactly when faults occur has attracted considerable attention in recent years. Most related studies have focused on the use of the maximum likelihood estimator (MLE) method to determine the fault in univariate processes, in which the underlying process distribution should be known in advance. In addition, most studies have been devoted to identifying the faults of process mean shifts. Different from most of the current research, the present study proposes an effective approach to identify the faults of variance shifts in a multivariate process. The proposed mechanism comprises the analysis of variance (ANOVA) approach, a neural network (NN) classifier, and an identification strategy. To demonstrate the effectiveness of our proposed approach, a series of simulated experiments is conducted, and the best results from our proposed approach are addressed.