A multivariate exponentially weighted moving average control chart for photovoltaic processes

By J. L. Coleman, J. Nickerson. Conference Record of the Thirty-first IEEE Photovoltaic Specialists Conference, 2005., 2005

846 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4fed097e90c542fb41b028d8e37c07d6366f03d0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

In Statistics and Probability

Citations in the corpus

A multivariate EWMA control chart for monitoring process variability with individual observations (2005, methodology): “Alternative methods such as the Markov chain approximation have been used to calculate the ARL of multivariate control charts such as the MEWMA chart (Lowry et al., 1992; Rigdon, 1995a, 1995b; Runger and Prabhu, 1996; Prabhu and Runger, 1997).”
NONPARAMETRIC CONTROL CHART FOR MONITORING PROFILES USING CHANGE POINT FORMULATION AND ADAPTIVE SMOOTHING (2009, background): “multivariate vector (cf., Lowry et al. 1992 ), referred to as the MEW chart.”
A General Multivariate Exponentially Weighted Moving-Average Control Chart (2007, methodology): “This study illustrates the potential benefits of generalizing the multivariate EWMA technique of Lowry et al. (1992) by allowing nonzero off-diagonal elements in the smoothing matrix rather than restricting it to be diagonal.”
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series (2018, background): “nentially Weighted Moving ASverage (MEWMA) [11] were proposed to monitor the performance of multiple variables in a manufacturing system.”
Multivariate binomial/multinomial control chart (2014, methodology): “A natural idea is to first obtain the estimate of the coefficient vector β for each sample and then apply the multivariate EWMA chart (Lowry et al., 1992) and some modifications of the LRT to these estimates of β at different time points.”
A New Multivariate Control Chart for Monitoring Both Location and Dispersion (2005, background): “Property 1 is based on Lowry’s (1989) results where she showed that the joint distribution of Ti depends on and only through the value of the noncentrality parameter 2.”
A New EWMA Control Chart for Monitoring Both Location and Dispersion (2004, background): “Montgomery [10] and Lowry [7] discussed the inertia problem associated with EWMA-type control charts.”