The control chart is a tool used to evaluate variation in a process and determine whether the variation is "usual" or "unusual." Control charts pattern (CCP) recognition is one of the most important tools in statistical process control to identify process problems. Most of the previous works in statistical process control applying artificial intelligence used raw data as input vector representations. The objective of this study was to evaluate the relative performance of a feature-based optimized statistical process control recognizer compared with the raw data-based optimized recognizer. The study focused on recognition of seven control chart patterns plotted on the X-bar chart. The Artificial neural network based pattern recognizer trained using the six selected statistical features resulted in significantly better performance compared with the raw data-based recognizer for the considered five algorithms
Control chart pattern recognition, neural network, backpropagation, statistical features,
