Self-oriented control charts for efficient monitoring of mean vectors.
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Date
2014
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Abstract
This work presents a procedure for monitoring the centre of multivariate processes by optimising the
noncentrality parameter with respect to the maximum separability between the in- and out-of-control
states. Similarly to the Principal Component Analysis, this procedure is a linear transformation but using
a different criterion which maximises the trace of two scatter matrices. The proposed linear statistic is
self-oriented in the sense that no prior information is given, then it is monitored by two types of control
charts aiming to identify small and intermediate shifts. As the control charts performances depend only
on the noncentrality parameter, comparisons are made with traditional quadratic approaches, such as the
Multivariate Cumulative Sum (MCUSUM), the Multivariate Exponentially Weighted Moving Average
(MEWMA) and Hotelling’s T2 control chart. The results show that the proposed statistic is a solution
for the problem of finding directions to be monitored without the need of selecting eigenvectors,
maximising efficiency with respect to the average run length.
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Keywords
Quality control, Multivariate statistics, Mean vectors, Simulation, Average run lenght
Citation
MORAES, D. A. O. et al. Self-oriented control charts for efficient monitoring of mean vectors. Computers & Industrial Engineering, v. 75, p. 102-115, 2014. Disponível em: <http://www.sciencedirect.com/science/article/pii/S0360835214001880>. Acesso em: 13 abr. 2014.