A neural fuzzy control chart for detecting and classifying process mean shifts

We propose a neural fuzzy (NF) control chart for identifying process mean shifts. A supervised multi-layer backpropagation neural network is trained off-line to detect various mean shifts in a production process. In identifying mean shifts in real-time usage, the neural network's outputs are classified into various decision regions using a fuzzy set scheme. The approach offers better performance and additional advantages over conventional control charts. Simulation results show that the proposed NF control charts are superior to conventional X-bar charts and CUSUM charts in terms of the average run lengths (ARL). The proposed system also has the ability to identify the magnitude of a mean shift, in addition to the Shewhart-type control chart heuristic rules. Correct classification percentages are studied. Furthermore, general guidelines are given for the proper use of the proposed NF charts.