Multivariate time series analysis is applied to understand and model the dynamics of an electrolytic process manufacturing copper. Here, eight metal impurities were measured, twice daily, over a period of one year, to characterize the quality of Ε½ . the copper. In the data analysis, these eight vari
β¦ LIBER β¦
Multivariate Process Variability Monitoring
β Scribed by Djauhari, Maman A.; Mashuri, Muhammad; Herwindiati, Dyah E.
- Book ID
- 111933261
- Publisher
- Taylor and Francis Group
- Year
- 2008
- Tongue
- English
- Weight
- 276 KB
- Volume
- 37
- Category
- Article
- ISSN
- 0361-0926
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Multivariate process and quality monitor
β
Conny WikstrΓΆm; Christer Albano; Lennart Eriksson; HΓ₯kan FridΓ©n; Erik Johansson;
π
Article
π
1998
π
Elsevier Science
π
English
β 334 KB
Investigation of Dynamic Multivariate Ch
β
Lei XIE; Jianming ZHANG; Shuqing WANG
π
Article
π
2006
π
Elsevier Science
π
English
β 791 KB
Monitoring process variability using aux
β
Muhammad Riaz
π
Article
π
2007
π
Springer
π
English
β 305 KB
Effective Control Charts for Monitoring
β
Chia-Ling Yen; Jyh-Jen Horng Shiau; Arthur B. Yeh
π
Article
π
2011
π
John Wiley and Sons
π
English
β 625 KB
Multivariate Statistical Process Monitor
β
Liang, J.
π
Article
π
2008
π
Curtin University of Technology
π
English
β 426 KB
π 1 views
## Abstract In this paper, a general kernel density estimator has been introduced and discussed for multivariate processes in order to provide enhanced realβtime performance monitoring. The proposed approach is based upon the concept of kernel density function, which is more appropriate to the unde
Monitoring a complex refining process us
β
Ashraf AlGhazzawi; Barry Lennox
π
Article
π
2008
π
Elsevier Science
π
English
β 364 KB