A Bayesian framework for online parameter estimation and process adjustment using categorical observations

Jing Lin and Kaibo Wang

IISE Transactions, 2012, vol. 44, issue 4, 291-300

Abstract: In certain manufacturing processes, accurate numerical readings are difficult to collect due to time or resource constraints. Alternatively, low-resolution categorical observations can be obtained that can act as feasible and low-cost surrogates. Under such situations, all classic statistical quality control activities, such as model building, parameter estimation, and feedback adjustment, have to be done on the basis of these categorical observations. However, most existing statistical quality control methods are developed based on numerical observations and cannot be directly applied if only categorical observations are available. In this research, a new online approach for parameter estimation and run-to-run process control using categorical observations is developed. The new approach is built in the Bayesian framework; it provides a convenient way to update parameter estimates when categorical observations arrive gradually in a real production scenario. Studies of performance reveal that the new method can provide stable estimates of unknown parameters and achieve effective control performance for maintaining quality.

Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0740817X.2011.568039 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:44:y:2012:i:4:p:291-300

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/0740817X.2011.568039

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().