Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdo...

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Autores principales:	Salman Khalid, Hyunho Hwang, Heung Soo Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	real-world data data-driven machine learning thermal power plant optimal sensor selection boiler water wall tube turbine Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/4829374d28ff49a1b9b141e7d5c2a182
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id	oai:doaj.org-article:4829374d28ff49a1b9b141e7d5c2a182
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spelling	oai:doaj.org-article:4829374d28ff49a1b9b141e7d5c2a1822021-11-11T18:20:43ZReal-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant10.3390/math92128142227-7390https://doaj.org/article/4829374d28ff49a1b9b141e7d5c2a1822021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2814https://doaj.org/toc/2227-7390Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.Salman KhalidHyunho HwangHeung Soo KimMDPI AGarticlereal-world datadata-driven machine learningthermal power plantoptimal sensor selectionboiler water wall tubeturbineMathematicsQA1-939ENMathematics, Vol 9, Iss 2814, p 2814 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	real-world data data-driven machine learning thermal power plant optimal sensor selection boiler water wall tube turbine Mathematics QA1-939
spellingShingle	real-world data data-driven machine learning thermal power plant optimal sensor selection boiler water wall tube turbine Mathematics QA1-939 Salman Khalid Hyunho Hwang Heung Soo Kim Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant
description	Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.
format	article
author	Salman Khalid Hyunho Hwang Heung Soo Kim
author_facet	Salman Khalid Hyunho Hwang Heung Soo Kim
author_sort	Salman Khalid
title	Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant
title_short	Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant
title_full	Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant
title_fullStr	Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant
title_full_unstemmed	Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant
title_sort	real-world data-driven machine-learning-based optimal sensor selection approach for equipment fault detection in a thermal power plant
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4829374d28ff49a1b9b141e7d5c2a182
work_keys_str_mv	AT salmankhalid realworlddatadrivenmachinelearningbasedoptimalsensorselectionapproachforequipmentfaultdetectioninathermalpowerplant AT hyunhohwang realworlddatadrivenmachinelearningbasedoptimalsensorselectionapproachforequipmentfaultdetectioninathermalpowerplant AT heungsookim realworlddatadrivenmachinelearningbasedoptimalsensorselectionapproachforequipmentfaultdetectioninathermalpowerplant
_version_	1718431880793030656

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

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