Abstract
The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns (MCCPs) are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network (NN) based scheme to enhance the accurate identification rate (AIR) for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine (SVM), and multivariate adaptive regression splines (MARS). Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system.
Highlights
IntroductionHigh-quality products are typically manufactured through stable and effective industrial processes
An industrial statistical process control (SPC)-engineering process control (EPC) process is assumed to be disturbed by five single disturbances that are described by (5)
This study focuses on identifying three mixture control chart patterns (MCCPs): {CYC, SYS}, {CYC, SHI}, and {SYS, SHI}
Summary
High-quality products are typically manufactured through stable and effective industrial processes. Difficulty may be encountered when typical SPC charts are used to monitor an autocorrelated process. From a quality and control engineering viewpoint, SPC and EPC are two very important techniques for maintaining the quality of an industrial process. The primary function of EPC is to compensate for the effects of process disturbances by manipulating controllable variables [1, 2]. They have their own individual features and specialties, SPC and EPC are naturally linked due to their roles in monitoring and controlling industrial processes. The goal-oriented integration of these techniques has been studied extensively in recent decades [1,2,3,4]
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