On-line concurrent control chart pattern recognition using singular spectrum analysis and random forest

Abstract

A scheme integrated with engineer process control (EPC) is a useful approach to identify the disturbances for statistical process control (SPC) charting of an auto-correlated process. Although the EPC is able to compensate for the underlying disturbance, the underlying disturbance is embedded in the control chart. Thus, this makes the control chart patterns (CCPs) difficult to be identified. As documented in the literature, much efforts have been put in the recognition of single basic patterns of unnatural variation. However, there could exist a combination of two unnatural patterns simultaneously in a real-world manufacturing process. Also for an automated real-time production line, data are collected automatically and monitored by a computer-based system. Thus, the early detection of abnormality is of high importance for the aforesaid problem. This study aims to establish an on-line detection system used for monitoring the mixture unnatural CCPs for a SPC-EPC process. This paper presents a hybrid approach based on singular spectrum analysis (SSA) and random forest (RF) to identify the concurrent CCPs in an on-line SPC-EPC process. A total of fifteen types of concurrent CCPs were utilized to validate the proposed method. The SSA method was also used to decompose the mixture patterns into single patterns. The RF was employed to identify the types of patterns to which it belongs. The results showed that the proposed method was able to handle most of the concurrent CCPs very successfully with an average accurate identification rate of 91.8%. Also, the proposed method was found to be more accurate and efficient than the use of the hybrid method of SSA and support vector machine (SVM). It is suggested that this proposed system could be possibly applied for monitoring an on-line process to a greater extent.