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| Wykres kontrolny odporny (Robust Control Chart)× | Analiza zdolności procesu odpornej na wartości odstające× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1989–1997 (foundational period) | 1990s–2000s |
| Twórca≠ | David M. Rocke; L. G. Tatum (key contributors) | 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) |
| Typ≠ | Statistical process monitoring technique | Quantitative quality engineering method |
| Źródło pierwotne≠ | Tatum, L. G. (1997). Robust estimation of the process standard deviation for control charts. Technometrics, 39(2), 127–141. DOI ↗ | 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 ↗ |
| Inne nazwy | robust Shewhart chart, outlier-resistant control chart, robust SPC chart, distribution-free control chart | Robust PCA, Robust Capability Indices, Outlier-Resistant Capability Analysis, Robust Cpk Analysis |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | A robust control chart replaces the classical mean and standard deviation estimators in a Shewhart-style chart with resistant alternatives — such as the median and median absolute deviation (MAD) — so that a small fraction of outliers or non-normal process data cannot distort the control limits. The approach preserves the real-time monitoring logic of standard control charts while protecting against inflated or deflated limits caused by contaminated Phase I reference data. | 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. |
| ScholarGateZbiór danych ↗ |
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