https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
Energy Pvt Ltd Sahiwal Coal Power Complex, Huaneng Shandong Ruyi Pakistan, Sahiwal 57000, Punjab, Pakistan.
Univ Engn & Technol, Dept Mech Engn, Taxila 47080, Punjab, Pakistan.
Univ Engn & Technol, Dept Mech Engn, Lahore 54890, Punjab, Pakistan.
Natl Univ Singapore, Dept Mech Engn, Singapore 117575, Singapore.
Show others and affiliations
2022 (English)In: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 61, no 3, p. 1864-1880Article in journal (Refereed) Published
Abstract [en]

The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine's bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment. (c) 2021 THE AUTHORS. Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Place, publisher, year, edition, pages
2022. Vol. 61, no 3, p. 1864-1880
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-57734DOI: 10.1016/j.aej.2021.07.0391110-0168ISI: 000765309500007Scopus ID: 2-s2.0-85112569410OAI: oai:DiVA.org:mdh-57734DiVA, id: diva2:1650158
Available from: 2022-04-06 Created: 2022-04-06 Last updated: 2024-05-29Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Salman, Chaudhary Awais

Search in DiVA

By author/editor
Salman, Chaudhary Awais
By organisation
Future Energy Center
In the same journal
Alexandria Engineering Journal
Energy Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 66 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf