Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ робастной пригодности процесса× | Shewhart Charts× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1990s–2000s | 1924 (first use); 1931 (seminal book) |
| Автор метода≠ | Extended from classical PCA (Kane, 1986; Juran, 1974) via robust statistics (Huber, 1981); formalized for capability indices by Tong & Chen (1998) and Pearn & Kotz (1994) | Walter A. Shewhart (Bell Labs) |
| Тип≠ | Quantitative quality engineering method | Statistical monitoring and control technique |
| Основополагающий источник≠ | Maravelakis, P. E., Bersimis, S., Panaretos, J., & Psarakis, S. (2004). Identifying the out of control variable in a multivariate control chart. Communications in Statistics - Theory and Methods, 33(10), 2499–2510. link ↗ | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. link ↗ |
| Другие названия | Robust PCA, Robust Capability Indices, Outlier-Resistant Capability Analysis, Robust Cpk Analysis | Shewhart chart, process-behavior chart, SPC chart, quality control chart |
| Связанные | 6 | 6 |
| Сводка≠ | Robust process capability analysis extends classical capability indices (Cp, Cpk, Ppk) by replacing the sample mean and standard deviation with robust location and scale estimators — such as the median, trimmed mean, MAD, or IQR-based spread — so that outliers and non-normal process distributions do not inflate or distort the capability estimate. The result is a more reliable assessment of whether a manufacturing or service process can consistently meet specification limits. | A control chart is a time-series graph with statistically derived upper and lower control limits that separates the natural, random variation of a process (common cause) from unusual, assignable variation (special cause). Invented by Walter Shewhart at Bell Labs in 1924, control charts remain the foundational tool of Statistical Process Control and are used across manufacturing, healthcare, software, and service industries to monitor whether a process remains stable and predictable over time. |
| ScholarGateНабор данных ↗ |
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