Author:
Xu Shu,Lu Bo,Baldea Michael,Edgar Thomas F.,Wojsznis Willy,Blevins Terrence,Nixon Mark
Abstract
AbstractIn the past decades, process engineers are facing increasingly more data analytics challenges and having difficulties obtaining valuable information from a wealth of process variable data trends. The raw data of different formats stored in databases are not useful until they are cleaned and transformed. Generally, data cleaning consists of four steps: missing data imputation, outlier detection, noise removal, and time alignment and delay estimation. This paper discusses available data cleaning methods that can be used in data pre-processing and help overcome challenges of “Big Data”.
Subject
General Chemical Engineering
Reference532 articles.
1. Self - organizing maps Springer series in information rd ed Verlag;Kohonen;sciences Physica,1999
2. real - time estimation approach to time - varying time delay and parameters of NARX processes;Zhou;Comput Chem Eng,2000
3. Inferential sensor design in the presence of missing data : a case study;Lopes;Chemometr Intell Lab Syst,2005
4. Multivariate process trajectories capture resolution analysis;Bogomolov;Chemometr Intell Lab Syst,2011
5. Manne Missing values in principal component analysis;Grung;Chemometr Intell Lab Syst,1998
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