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In multivariate statistical process control (MSPC), most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon an overall statistic. But these charts do not relieve the need for pinpointing source(s) of the out-of-control signals. Neural networks (NNs) have excellent noise tolerance and high pattern identification capability in real time, which...
Unnatural patterns exhibited by control charts can be associated with certain assignable causes for process variation. Hence, accurately recognizing control chart patterns (CCPs) can significantly narrow down the scope of possible causes, and speeds up the troubleshooting process. This paper proposes a selective neural network (NN) ensemble approach DPSOEN, which employs a collection of several NNs...
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