Fault identification in industrial processes using an integrated approach of neural network and analysis of variance

Abstract

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. © 2013 Yuehjen E. Shao and Chia-Ding Hou.