Introduction to Statistical Quality Control

By F. Spiring. Technometrics, 2007

4,901 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/370cdce73e60fa98ab41410b93a2a52cb231cef7

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Citations in the corpus

A multivariate EWMA control chart for monitoring process variability with individual observations (2005, background): “For a general discussion, see, for example, Ryan (2000) and also Montgomery (2001).”
A study of EWMA chart with transformed exponential data (2007, background): “Montgomery ( 2005 ) explained the idea of monitoring time between events data by transformation method, and stated that ‘in many cases, the CUSUM and EWMA control charts would be better alternatives because these charts are more effective in detecting small shifts in mean’.”
Robust CUSUM Control Charting (2013, methodology): “…two plotting statistics are defined as: Cþi ¼ max 0; Xi l0ð Þ K þ Cþi 1½ C i ¼ max 0; Xi l0ð Þ K þ C i 1 ; ½1 where i is the subgroup number, Xi is the mean of study variable X, l0 is the target mean of the study variable X, and K is the reference value of the CUSUM scheme (cf. Montgomery 2009).”
Monitoring correlated processes with binomial marginals (2009, methodology): “For a detailed description, consult Montgomery [6].”
EWMA Charts for the Weibull Shape Parameter (2010, background): “In practice, values of λ between 0.05 and 0.25 work well (Montgomery (2005), p. 411).”
Control Charts for Poisson Count Data with Varying Sample Sizes (2010, methodology): “Often Lu = 3, resulting in the usual three-sigma limits (Montgomery (2009)).”
Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry (2008, methodology): “In industrial processes, data-based process monitoring methods have been widely used (Montgomery, 1997).”
Developing a variables repetitive group sampling plan based on process capability index C pk with unknown mean and variance (2013, background): “But the former provides smaller sample size when compared with attribute acceptance sampling plans and this reduction offsets the increased cost (see, e.g. Pearn and Wu [1], Wu and Pearn [2] and Montgomery [3]).”
Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges (2016, methodology): “In order to cope with dimensionality reducion, statistical approaches such as Principal Component Analsis (PCA) ( Montgomery, 2007 ) and Partial Least Squares (PLS) Venkatasubramanian et al. (2003a) ) are generally used.”
An introduction to structural health monitoring (2007, methodology): “…that include SHM, condition monitoring (CM; Bentley & Hatch 2003), non-destructive evaluation (NDE; Shull 2002), statistical process control (SPC; Montgomery 1997) and damage prognosis (DP; which is summarized within this theme issue in Farrar & Lieven (2007); see also Farrar et al. (2003)).”
Weighted-loss-function CUSUM chart for monitoring mean and variance of a production process (2005, methodology): “Their values are often chosen about halfway between the in-control values and the specified out-of-control values of the statistics I, D and V (Montgomery 2001).”
Design and implementation of CUSUM exceedance control charts for unknown location (2014, methodology): “NPCUSUM-Rank chart for Montgomery (2001) data with d = 0.”
Combined Shewhart–EWMA control charts with estimated parameters (2010, methodology): “Statistical control charts are widely used tools for monitoring process and quality improvement (see [1]).”
Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds (2015, methodology): “In the following subsections we mention relevant algorithms used in online learning, which fall in two main categories: window strategies and weightedinstance approaches.”
The CUSUM and the EWMA Head-to-Head (2014, background): “This failing of Shewhart charts comes from their limitation to the information from only the most recent rational group, ignoring any information contained earlier in the data sequence (Montgomery 2013).”