dc.contributor.author |
Razmy, A.M. |
|
dc.date.accessioned |
2018-02-26T04:44:00Z |
|
dc.date.available |
2018-02-26T04:44:00Z |
|
dc.date.issued |
2017-12-07 |
|
dc.identifier.citation |
7th International Symposium 2017 on “Multidisciplinary Research for Sustainable Development”. 7th - 8th December, 2017. South Eastern University of Sri Lanka, University Park, Oluvil, Sri Lanka. pp. 256-263. |
en_US |
dc.identifier.isbn |
978-955-627-120-1 |
|
dc.identifier.uri |
http://ir.lib.seu.ac.lk/handle/123456789/3055 |
|
dc.description.abstract |
In quality control, joint monitoring of process mean and variance has become more
popular due to the disadvantages of monitoring mean and variance alone. This paper introduce a
new joint monitoring scheme for process mean and variance. In this new scheme, exponential
moving average technique is applied to the statistics D2t developed in the Shewhart distance
scheme by Razmy (2010). The optimal design parameter were found through simulations for
designing this new scheme. The design techniques of this scheme are illustrated with an example.
A big advantage of this new scheme is, unlike most existing joint monitoring schemes, the design
parameters of this scheme are independent on the sample size thus the quality engineers have
fewer constraints in implementing this scheme. |
en_US |
dc.language.iso |
en_US |
en_US |
dc.publisher |
South Eastern University of Sri Lanka, University Park, Oluvil, Sri Lanka. |
en_US |
dc.subject |
Average run length |
en_US |
dc.subject |
Control limit |
en_US |
dc.subject |
Exponential weighted moving average |
en_US |
dc.subject |
Joint monitoring |
en_US |
dc.title |
Exponentially weighted moving average distance square scheme for joint monitoring of process mean and variance |
en_US |
dc.type |
Article |
en_US |