On-line identification of control chart patterns using self-organizing approaches

Abstract

Effective identification of unnatural control chart patterns (CCPs) is an important issue in statistical process control, as unnatural CCPs can be associated with specific assignable causes adversely affecting the process. The intention of this paper is to develop an automatic CCP identification system using self-organizing approaches—neural network and decision tree (DT) learning. Recently, back-propagation networks (BPNs) have been widely used in the research field of CCP identification. However, one of the major limitations of conventional BPN is in dealing with dynamic patterns that vary over time, such as CCPs. This limitation is one of the major reasons for the false classification problem commonly encountered in the BPN-based CCP identification schemes in the literature. A time-lagging input algorithm is proposed in this research to enhance the performances of the BPN-based CCP identifiers. Additionally, DT learning is employed as a novel approach to the CCP identification problem. The simulation experiments demonstrate that both the BPN-based system with time-lagging input and the DT-based system perform better than the conventional BPN-based system in terms of identification accuracy and speed. The proposed time-lagging input algorithm can greatly improve the identification speed and stability of the BPN-based CCP identifier. Besides, the empirical comparison indicates that the DT-based system outperforms the BPN-based system with respect to classification capability in an on-line CCP identification scheme. Moreover, the learning time of the DT-based system is much shorter than that of the BPN-based system.