方法对比

	混合统计过程控制	CUSUM 控制图	统计过程控制
领域	实验设计	统计学	实验设计
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1990s–2000s	1954	1924–1931
提出者	Evolved from classical SPC (Shewhart, 1920s); hybrid extensions developed broadly from the 1990s onward by researchers including Montgomery, Woodall, and various neural-network SPC authors	E. S. Page	Walter A. Shewhart
类型	Process monitoring and control methodology	Statistical process control chart for small shifts	Process monitoring and quality control method
开创性文献	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0-470-16992-6	Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1/2), 100–115.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
别名	Hybrid SPC, combined SPC, integrated SPC, hybrid process monitoring	cumulative sum chart, CUSUM control chart, Page's CUSUM, kümülatif toplam kontrol kartı	SPC, statistical quality control, process control charting, Shewhart control
相关	2	4	6
摘要	Hybrid Statistical Process Control integrates classical control-chart methods (Shewhart, CUSUM, EWMA) with complementary techniques — such as neural networks, fuzzy logic, economic design, or multivariate statistics — to monitor and control manufacturing or service processes more effectively than any single approach alone. The hybrid architecture addresses known weaknesses of conventional SPC, including slow detection of small shifts, pattern-recognition limitations, and inability to handle non-normal or autocorrelated data.	The cumulative sum (CUSUM) control chart, introduced by E. S. Page in 1954, monitors a process by accumulating the deviations of observations from a target value rather than judging each point in isolation. Because small persistent shifts add up over time, the running sum makes them visible far sooner than a Shewhart chart, making CUSUM the tool of choice for detecting small, sustained changes in the process mean.	Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers.