Programs
- M. Tech. in Automotive Engineering -
- Clinical Fellowship in Laboratory Genetics & Genomics - Fellowship
Publication Type : Journal Article
Publisher : Journal of Intelligent & Fuzzy Systems
Source : Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5731-5736, 2021
Url : https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs189892
Keywords : Data reconciliation, MA-PCA, EWMA-PCA
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2021
Abstract : Abstract: In process industries, measurements usually contain errors due to the improper instrumental variation, physical leakages in process streams and nodes, and inaccurate recording/reporting. Thus, these measurements violate the laws of conservation, and do not conform to process constraints. Data reconciliation (DR) is used to resolve the difference between measurements and constraints. DR is also used in reducing the effect of random errors and more accurately estimating the true values. A multivariate technique that is used to obtain estimates of true values while preserving the most significant inherent variation is Principal Component Analysis (PCA). PCA is used to reduce the dimensionality of the data with minimum information loss. In this paper, two new DR techniques are proposed moving-average PCA (MA-PCA) and exponentially weighted moving average PCA (EWMA-PCA) to improve the performance of DR and obtain more accurate and consistent data. These DR techniques are compared based on RMSE. Further, these techniques are analyzed for different values of sample size, weighting factor, and variances.
Cite this Research Publication : Jeyanthi, R. et al. ‘Data Reconciliation Using MA-PCA and EWMA-PCA for Large Dimensional Data’. Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5731-5736, 2021