NDLI: A new statistical process control method to monitor and diagnose bivariate normal mean vectors and covariance matrices simultaneously

Content Provider	Springer Nature Link
Author	Akhavan Niaki, Seyed Taghi Ostadsharif Memar, Ahmad
Copyright Year	2008
Abstract	In this paper, in order to find an adequate method of monitoring the mean vector and covariance matrix of a production process simultaneously, first, some available univariate control methods were reviewed and evaluated. Then, the maximum exponentially weighted moving average method with a better potential application and good performances in terms of average time to signal (ATS) criterion was selected to be extended to the bivariate case. In the extended procedure, by proper transformation of the control parameters, the primary control space is transformed such that all control elements have the same probability distributions. In this case, only the maximum absolute value of the transformed elements is monitored to control both the mean vector and the covariance matrix simultaneously. Moreover, a heuristic procedure is proposed to interpret and diagnose the out-of-control parameter(s). Finally, not only the performances of the proposed method are evaluated and compared to the ones from the two recent methods in terms of ATS but also the percentage of correct classification of the proposed method was estimated by extensive simulation experiments.
Starting Page	964
Ending Page	981
Page Count	18
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	43
Issue Number	9-10
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-10-18
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Statistical quality control Simultaneous control of mean and variance Multivariate quality control Maximum exponentially weighted moving average method Signal diagnosing Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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