Extracting knowledge from historical databases for process monitoring using feature extraction and data clustering. Thomas, M. C. & Romagnoli, J. In Kravanja, Z. & Bogataj, M., editors, Computer Aided Chemical Engineering, volume 38, of 26 European Symposium on Computer Aided Process Engineering, pages 859–864. Elsevier, January, 2016. ![Extracting knowledge from historical databases for process monitoring using feature extraction and data clustering [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex For most chemical plants, a major obstacle inhibiting the application of cutting edge fault detection and diagnosis is that many of the best methods require data organized into groups before training is possible. Data clustering and non-linear dimensionality reduction are underutilized tools for this task and this study evaluates how they can work in tandem to extract knowledge from chemical process data sets. Two non-linear dimensionality reduction techniques and principal component analysis as well as two clustering techniques are studied on industrial case studies and a simulation
@incollection{thomas_extracting_2016,
series = {26 {European} {Symposium} on {Computer} {Aided} {Process} {Engineering}},
title = {Extracting knowledge from historical databases for process monitoring using feature extraction and data clustering},
volume = {38},
url = {https://www.sciencedirect.com/science/article/pii/B978044463428350148X},
abstract = {For most chemical plants, a major obstacle inhibiting the application of cutting edge fault detection and diagnosis is that many of the best methods require data organized into groups before training is possible. Data clustering and non-linear dimensionality reduction are underutilized tools for this task and this study evaluates how they can work in tandem to extract knowledge from chemical process data sets. Two non-linear dimensionality reduction techniques and principal component analysis as well as two clustering techniques are studied on industrial case studies and a simulation},
language = {en},
urldate = {2022-05-02},
booktitle = {Computer {Aided} {Chemical} {Engineering}},
publisher = {Elsevier},
author = {Thomas, Michael C. and Romagnoli, Jose},
editor = {Kravanja, Zdravko and Bogataj, Miloš},
month = jan,
year = {2016},
doi = {10.1016/B978-0-444-63428-3.50148-X},
keywords = {data clustering, data mining, dimensionality reduction, feature extraction},
pages = {859--864},
}
Downloads: 0
{"_id":"nPJrBnq9jF2njuaCD","bibbaseid":"thomas-romagnoli-extractingknowledgefromhistoricaldatabasesforprocessmonitoringusingfeatureextractionanddataclustering-2016","author_short":["Thomas, M. C.","Romagnoli, J."],"bibdata":{"bibtype":"incollection","type":"incollection","series":"26 European Symposium on Computer Aided Process Engineering","title":"Extracting knowledge from historical databases for process monitoring using feature extraction and data clustering","volume":"38","url":"https://www.sciencedirect.com/science/article/pii/B978044463428350148X","abstract":"For most chemical plants, a major obstacle inhibiting the application of cutting edge fault detection and diagnosis is that many of the best methods require data organized into groups before training is possible. Data clustering and non-linear dimensionality reduction are underutilized tools for this task and this study evaluates how they can work in tandem to extract knowledge from chemical process data sets. Two non-linear dimensionality reduction techniques and principal component analysis as well as two clustering techniques are studied on industrial case studies and a simulation","language":"en","urldate":"2022-05-02","booktitle":"Computer Aided Chemical Engineering","publisher":"Elsevier","author":[{"propositions":[],"lastnames":["Thomas"],"firstnames":["Michael","C."],"suffixes":[]},{"propositions":[],"lastnames":["Romagnoli"],"firstnames":["Jose"],"suffixes":[]}],"editor":[{"propositions":[],"lastnames":["Kravanja"],"firstnames":["Zdravko"],"suffixes":[]},{"propositions":[],"lastnames":["Bogataj"],"firstnames":["Miloš"],"suffixes":[]}],"month":"January","year":"2016","doi":"10.1016/B978-0-444-63428-3.50148-X","keywords":"data clustering, data mining, dimensionality reduction, feature extraction","pages":"859–864","bibtex":"@incollection{thomas_extracting_2016,\n\tseries = {26 {European} {Symposium} on {Computer} {Aided} {Process} {Engineering}},\n\ttitle = {Extracting knowledge from historical databases for process monitoring using feature extraction and data clustering},\n\tvolume = {38},\n\turl = {https://www.sciencedirect.com/science/article/pii/B978044463428350148X},\n\tabstract = {For most chemical plants, a major obstacle inhibiting the application of cutting edge fault detection and diagnosis is that many of the best methods require data organized into groups before training is possible. Data clustering and non-linear dimensionality reduction are underutilized tools for this task and this study evaluates how they can work in tandem to extract knowledge from chemical process data sets. Two non-linear dimensionality reduction techniques and principal component analysis as well as two clustering techniques are studied on industrial case studies and a simulation},\n\tlanguage = {en},\n\turldate = {2022-05-02},\n\tbooktitle = {Computer {Aided} {Chemical} {Engineering}},\n\tpublisher = {Elsevier},\n\tauthor = {Thomas, Michael C. and Romagnoli, Jose},\n\teditor = {Kravanja, Zdravko and Bogataj, Miloš},\n\tmonth = jan,\n\tyear = {2016},\n\tdoi = {10.1016/B978-0-444-63428-3.50148-X},\n\tkeywords = {data clustering, data mining, dimensionality reduction, feature extraction},\n\tpages = {859--864},\n}\n\n","author_short":["Thomas, M. C.","Romagnoli, J."],"editor_short":["Kravanja, Z.","Bogataj, M."],"key":"thomas_extracting_2016","id":"thomas_extracting_2016","bibbaseid":"thomas-romagnoli-extractingknowledgefromhistoricaldatabasesforprocessmonitoringusingfeatureextractionanddataclustering-2016","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/B978044463428350148X"},"keyword":["data clustering","data mining","dimensionality reduction","feature extraction"],"metadata":{"authorlinks":{}}},"bibtype":"incollection","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["data clustering","data mining","dimensionality reduction","feature extraction"],"search_terms":["extracting","knowledge","historical","databases","process","monitoring","using","feature","extraction","data","clustering","thomas","romagnoli"],"title":"Extracting knowledge from historical databases for process monitoring using feature extraction and data clustering","year":2016}