A Neural Network ensemble for classifying source(s) in multivariate manufacturing processes

In multivariate statistical process control, most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon an overall statistics. But these charts do not relieve the need for pinpointing the source(s) of the out-of-control signals. Neural networks (NNs) have excellent noise tolerance and high pattern identification capability, which have been used successfully in MSPC. This study proposed a selective NNs ensemble approach DPSOEN, where several NNs selected are jointly used to classify source(s) of out-of-control signals in multivariate processes. Extensive experiment is also carried out to examine effects of six statistical features on the performance of DPSOEN. The investigation proposed a heuristic approach for applying DPSOEN as an effective tool to identify abnormal source(s) in bivariate SPC with potential application for MSPC in general.