A hybrid system for SPC concurrent pattern recognition

Abstract

Any nonrandom patterns shown in Statistical Process Control (SPC) charts imply possible assignable causes that may deteriorate the process performance. Hence, timely detecting and recognizing Control Chart Patterns (CCPs) for nonrandomness is very important in the implementation of SPC. Due to the limitations of run-rule-based approaches, Artificial Neural Networks (ANNs) have been resorted for detecting CCPs. However, most of the reported ANN approaches are only limited to recognize single basic patterns. Different from these approaches, this paper presents a hybrid approach by integrating wavelet method with ANNs for on-line recognition of CCPs including concurrent patterns. The main advantage of this approach is its capability of recognizing coexisted or concurrent patterns without training by concurrent patterns. The test results using simulated data have demonstrated the improvements and the effectiveness of the methodology with a success rate up to 91.41% in concurrent CCP recognition.