A Neural Network Ensemble Approach for the Recognition of SPC Chart Patterns

Abstract

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 trained for CCP identifications. DPSOEN provides more simple training and better performance than single NN. To further improve the performance of recognizers, several statistical features extracted from raw observations are used in the representation of input features. The simulation results indicate that integration of raw data and statistical features-based DPSOEN shows the best performance. Analysis from this study provides guidelines in developing NN ensemble-based SPC recognition systems.