Abstract
The problem of using time-varying trajectory data measured on many process variables over the finite duration of a batch process is considered. Multiway principal-component analysis is used to compress the information contained in the data trajectories into low-dimensional spaces that describe the operation of past batches. This approach facilitates the analysis of operational and quality-control problems in past batches and allows for the development of multivariate statistical process control charts for on-line monitoring of the progress of new batches. Control limits for the proposed charts are developed using information from the historical reference distribution of past successful batches. The method is applied to data collected from an industrial batch polymerization reactor.
Keywords
Control chartStatistical process controlComputer sciencePrincipal component analysisMultivariate statisticsProcess (computing)Data miningProcess controlBatch processingProcess capabilityStatisticsWork in processMathematicsEngineeringOperations managementArtificial intelligenceMachine learning
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Publication Info
- Year
- 1995
- Type
- article
- Volume
- 37
- Issue
- 1
- Pages
- 41-41
- Citations
- 228
- Access
- Closed
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Cite This
Paul Nomikos,
John F. MacGregor
(1995).
Multivariate SPC Charts for Monitoring Batch Processes.
Technometrics
, 37
(1)
, 41-41.
https://doi.org/10.2307/1269152
Identifiers
- DOI
- 10.2307/1269152