Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing

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)...

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Autores principales:	Waqar Muhammad Ashraf, Yasir Rafique, Ghulam Moeen Uddin, Fahid Riaz, Muhammad Asim, Muhammad Farooq, Abid Hussain, Chaudhary Awais Salman
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	ANN SVM Modeling & optimization Vibration reduction Steam turbine Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/23df474415e64a87b22ec471080a83e2
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id	oai:doaj.org-article:23df474415e64a87b22ec471080a83e2
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spelling	oai:doaj.org-article:23df474415e64a87b22ec471080a83e22021-12-02T04:59:43ZArtificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing1110-016810.1016/j.aej.2021.07.039https://doaj.org/article/23df474415e64a87b22ec471080a83e22022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821005093https://doaj.org/toc/1110-0168The 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.Waqar Muhammad AshrafYasir RafiqueGhulam Moeen UddinFahid RiazMuhammad AsimMuhammad FarooqAbid HussainChaudhary Awais SalmanElsevierarticleANNSVMModeling & optimizationVibration reductionSteam turbineEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 3, Pp 1864-1880 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	ANN SVM Modeling & optimization Vibration reduction Steam turbine Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	ANN SVM Modeling & optimization Vibration reduction Steam turbine Engineering (General). Civil engineering (General) TA1-2040 Waqar Muhammad Ashraf Yasir Rafique Ghulam Moeen Uddin Fahid Riaz Muhammad Asim Muhammad Farooq Abid Hussain Chaudhary Awais Salman Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
description	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.
format	article
author	Waqar Muhammad Ashraf Yasir Rafique Ghulam Moeen Uddin Fahid Riaz Muhammad Asim Muhammad Farooq Abid Hussain Chaudhary Awais Salman
author_facet	Waqar Muhammad Ashraf Yasir Rafique Ghulam Moeen Uddin Fahid Riaz Muhammad Asim Muhammad Farooq Abid Hussain Chaudhary Awais Salman
author_sort	Waqar Muhammad Ashraf
title	Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
title_short	Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
title_full	Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
title_fullStr	Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
title_full_unstemmed	Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
title_sort	artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/23df474415e64a87b22ec471080a83e2
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Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing

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