Advanced data analysis and process monitoring across manufacturing plants using multilevel methods

Three‐level versions of Multilevel Simultaneous Component Analysis (MLSCA) and Multilevel Partial Least Squares (MLPLS) were developed, which are capable of separating between‐plant, between‐run and within‐run process variation, and modeling these three levels in a multivariate way. In comparison to...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of chemometrics Ročník 30; číslo 8; s. 451 - 461
Hlavný autor: de Noord, Onno E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester Blackwell Publishing Ltd 01.08.2016
Wiley Subscription Services, Inc
Predmet:
Added value
Construction
Data analysis
Industrial plants
Least squares method
Manufacturing
Manufacturing engineering
Manufacturing industry
Modelling
Monitoring
Multilevel
process data analysis
process monitoring
Statistical process control
three-level MLPLS
three-level MLSCA
ISSN:0886-9383, 1099-128X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Three‐level versions of Multilevel Simultaneous Component Analysis (MLSCA) and Multilevel Partial Least Squares (MLPLS) were developed, which are capable of separating between‐plant, between‐run and within‐run process variation, and modeling these three levels in a multivariate way. In comparison to the two‐level versions they allow to discriminate between overall differences between plants and the variation between runs within a plant. It was shown that the three‐level version of MLSCA has clear added value for the analysis of process runs from different plants. In MLPLS other projections of the multivariate data onto latent variables and different views of the data are obtained when relevant Y information is available. This has clear added value for obtaining insight into the relation between process data and Y. A special use of MLPLS is to diagnose aberrations in first principles models. In batch process monitoring MLSCA at three levels allows simultaneous multivariate modelling of batch data from different manufacturing plants. By filtering out the between‐plant and between‐run sources of variation, and using only within‐run variation, monitoring models can be improved. Using within‐run data, it is possible to build monitoring models across manufacturing units and reduce the number of nuisance alarms, while improving abnormal situation detection and diagnosis. Model transfer is only possible if static between‐plant differences exist, but not if there are dynamic differences. Multilevel Simultaneous Component Analysis (MLSCA) and Multilevel Partial Least Squares (MLPLS) have been extended to three levels. This allows the separation of process data into between‐plant, between‐run and within‐run sources of variation, which has great benefits for multivariate process data analysis and process monitoring. The methods can be applied to data from continuous and batch processes.
Bibliografia:Supporting info item
istex:3412AC82C0EC71297AE94CD80A68B10467E7F696
ark:/67375/WNG-68P3PQH5-F
ArticleID:CEM2813
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0886-9383
1099-128X
DOI:10.1002/cem.2813