Abstract
Because of the excellent performance on monitoring and controlling an autocorrelated process, the integration of statistical process control (SPC) and engineering process control (EPC) has drawn considerable attention in recent years. Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of a process, especially when the underlying process is autocorrelated. However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized. Effective recognition of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process. In practical situations, after compensating by EPC, the underlying disturbance patterns could be of any mixture types which are totally different from the original patterns. This study proposes the integration of support vector machine (SVM) and artificial neural network (ANN) approaches to recognize the disturbance patterns of the underlying disturbances. Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system.
Highlights
Due to the fact that the process quality is mainly based on the detection and control of the disturbance, it is customized to develop various kinds of monitoring and controlling techniques in the past decade
We propose the combination of Statistical process control (SPC), engineering process control (EPC), and artificial neural network (ANN) as well as SPC, EPC, and support vector machine (SVM) to overcome the problems in an SPC/EPC system
This study combines the methodologies of SEA and SES to recognize the types of underlying disturbances for a SPC/EPC system
Summary
Due to the fact that the process quality is mainly based on the detection and control of the disturbance, it is customized to develop various kinds of monitoring and controlling techniques in the past decade. Statistical process control (SPC) chart is one of the most commonly used techniques to monitor and improve the quality of a process. A limitation of using traditional SPC charts is that they should monitor the independent process outputs [2,3,4,5]. If correlation among process outputs exists, the type I errors would be increased and the detecting capability of SPC charts is seriously decreased. One of the effective ways to deal with the difficulty in monitoring correlated outputs for SPC applications is to use the engineering process control (EPC) [11,12,13,14,15,16,17,18,19]. The use of a suitable EPC action would produce independent process outputs, and the problems of charting correlated outputs by SPC charts can be overcome
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