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.

与工程师过程控制 (EPC) 集成的方案是识别自相关过程的统计过程控制 (SPC) 图表干扰的有用方法。尽管 EPC 能够补偿潜在干扰，但潜在干扰嵌入在控制图中。因此，这使得难以识别控制图模式 (CCP)。正如文献中记载的那样，在识别非自然变异的单一基本模式方面已经付出了很多努力。然而，在现实世界的制造过程中可能同时存在两种非自然模式的组合。同样对于自动化实时生产线，数据由基于计算机的系统自动收集和监控。因此，对于上述问题，异常的早期检测是非常重要的。本研究旨在建立一个在线检测系统，用于监测 SPC-EPC 过程中混合物非天然 CCPs。本文提出了一种基于奇异谱分析 (SSA) 和随机森林 (RF) 的混合方法来识别在线 SPC-EPC 过程中的并发 CCP。总共使用了 15 种并发 CCP 来验证所提出的方法。SSA 方法也用于将混合模式分解为单个模式。RF 被用来识别它所属的模式类型。结果表明，所提出的方法能够非常成功地处理大多数并发的CCP，平均准确识别率为91.8%。此外，发现所提出的方法比使用 SSA 和支持向量机 (SVM) 的混合方法更准确和有效。