NOTE: This item is not available outside the Texas A&M University network. Texas A&M affiliated users who are off campus can access the item through NetID and password authentication or by using TAMU VPN. Non-affiliated individuals should request a copy through their local library's interlibrary loan service.
Monitoring process dispersion
dc.creator | Acosta Mejia, Cesar Alfonso | |
dc.date.accessioned | 2020-09-03T21:23:02Z | |
dc.date.available | 2020-09-03T21:23:02Z | |
dc.date.issued | 1996 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/DISSERTATIONS-1596309 | |
dc.description | Vita. | en |
dc.description.abstract | In this research the ARL performance of existing control charts for monitoring dispersion was evaluated by means of a Markov chain approximation and by integral equations. We analyzed and compared the performance of Shewhart charts such as R and S charts, CUSUM charts such as the CUSUM of S and the CUSUM of R, and the EWMA chart of the lnS2. For individual observations the MR chart, the EWMA chart presented by Wortham and Heinrich and the CUSUM chart presented by Hawkins are compared. We introduced the concept of ARL biased performance for dispersion control charts. We have found that traditional Shewhart charts for dispersion are generally ARL biased. EWMA charts for dispersion may also be ARL biased unless specific asymmetric limits are used. We present in this work new CUSUM charts for monitoring process dispersion. Some new charts result from a normalizing transformation of the subgroup variance. Another chart called the CPP CUSUM results from applying the likelihood ratio test to the change point problem. When no subgrouping is possible we present new alternatives such as the CUSUM of MR chart, and a modification of the CPP CUSUM chart. The comparison shows that the CPP CUSUM chart has the best ARL performance. Sensitivity analysis was performed to determine the performance of the CPP CUSUM chart when observations are non-normal or not independent. For non- normal observations we present a CUSUM chart for monitoring the kurtosis of processes following a symmetric class of distributions. We have also found that when the CPP CUSUM chart is carelessly applied to an AR(l) process its performance becomes ARL biased, Finally, we evaluate by means of simulation the ARL performance of the joint use of control charts for monitoring process location and dispersion. For this practice we found that the best and easiest way is the use of a pair of CUSUM charts. Although a pair of ARL unbiased EWMA charts is comparable. | en |
dc.format.extent | xiv, 144 leaves | en |
dc.format.medium | electronic | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.rights | This thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use. | en |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | Major industrial engineering | en |
dc.subject.classification | 1996 Dissertation A32 | |
dc.title | Monitoring process dispersion | en |
dc.type | Thesis | en |
thesis.degree.grantor | Texas A&M University | en |
thesis.degree.name | Doctor of Philosophy | en |
thesis.degree.name | Ph. D | en |
dc.type.genre | dissertations | en |
dc.type.material | text | en |
dc.format.digitalOrigin | reformatted digital | en |
dc.publisher.digital | Texas A&M University. Libraries | |
dc.identifier.oclc | 36427002 |
Files in this item
This item appears in the following Collection(s)
-
Digitized Theses and Dissertations (1922–2004)
Texas A&M University Theses and Dissertations (1922–2004)
Request Open Access
This item and its contents are restricted. If this is your thesis or dissertation, you can make it open-access. This will allow all visitors to view the contents of the thesis.