Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm

Owing to the fact that large-scale peak-load-regulation nuclear power turbine units’ thermal signal is greatly influenced by background noise and has non-stationary and nonlinear characteristics, this paper proposes a new fault diagnosis method for thermal sensors based on an improved independent co...

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Autores principales:	Yifan Wu, Kaiyu Wu, Wei Li, Jianhong Chen, Zitao Yu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	nuclear power unit thermal sensor fault diagnosis peak load regulation blind source separation random forest Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/27f762885ddc4bd1a174f97bd1944da0
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spelling	oai:doaj.org-article:27f762885ddc4bd1a174f97bd1944da02021-11-11T19:00:05ZPeak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm10.3390/s212169551424-8220https://doaj.org/article/27f762885ddc4bd1a174f97bd1944da02021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6955https://doaj.org/toc/1424-8220Owing to the fact that large-scale peak-load-regulation nuclear power turbine units’ thermal signal is greatly influenced by background noise and has non-stationary and nonlinear characteristics, this paper proposes a new fault diagnosis method for thermal sensors based on an improved independent component analysis (Improved-ICA) algorithm and random forest (RF) algorithm. This method is based on independent component analysis (ICA), which is not capable of extracting components independently. Therefore, we propose the use of the maximum approximate information negative entropy optimization model in order to improve the ICA algorithm’s independent principal component extraction ability and obtain better non-Gaussian physical source signal separation results. The improved ICA algorithm is used for the blind source separation of the thermal parameters of peak-load-regulation nuclear power units. A series of stationary physical source functions and a series of non-stationary noise signals are obtained. Then, according to the specific signal format and data volume of the nuclear power parameter signal, the network parameters of the random forest algorithm are determined, giving rise to the fault diagnosis model. Finally, the real-time operation data of an 1121 MW nuclear power unit are used to complete the training and fault diagnosis of the random forest network and analyze the diagnosis results. The results indicate that the model can effectively mine the abnormal sample points of thermal parameters and classify the fault type of the thermal sensor during peak load operation of the nuclear power unit. The accuracy rate is found to be at the threshold of 99%.Yifan WuKaiyu WuWei LiJianhong ChenZitao YuMDPI AGarticlenuclear power unitthermal sensor fault diagnosispeak load regulationblind source separationrandom forestChemical technologyTP1-1185ENSensors, Vol 21, Iss 6955, p 6955 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	nuclear power unit thermal sensor fault diagnosis peak load regulation blind source separation random forest Chemical technology TP1-1185
spellingShingle	nuclear power unit thermal sensor fault diagnosis peak load regulation blind source separation random forest Chemical technology TP1-1185 Yifan Wu Kaiyu Wu Wei Li Jianhong Chen Zitao Yu Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm
description	Owing to the fact that large-scale peak-load-regulation nuclear power turbine units’ thermal signal is greatly influenced by background noise and has non-stationary and nonlinear characteristics, this paper proposes a new fault diagnosis method for thermal sensors based on an improved independent component analysis (Improved-ICA) algorithm and random forest (RF) algorithm. This method is based on independent component analysis (ICA), which is not capable of extracting components independently. Therefore, we propose the use of the maximum approximate information negative entropy optimization model in order to improve the ICA algorithm’s independent principal component extraction ability and obtain better non-Gaussian physical source signal separation results. The improved ICA algorithm is used for the blind source separation of the thermal parameters of peak-load-regulation nuclear power units. A series of stationary physical source functions and a series of non-stationary noise signals are obtained. Then, according to the specific signal format and data volume of the nuclear power parameter signal, the network parameters of the random forest algorithm are determined, giving rise to the fault diagnosis model. Finally, the real-time operation data of an 1121 MW nuclear power unit are used to complete the training and fault diagnosis of the random forest network and analyze the diagnosis results. The results indicate that the model can effectively mine the abnormal sample points of thermal parameters and classify the fault type of the thermal sensor during peak load operation of the nuclear power unit. The accuracy rate is found to be at the threshold of 99%.
format	article
author	Yifan Wu Kaiyu Wu Wei Li Jianhong Chen Zitao Yu
author_facet	Yifan Wu Kaiyu Wu Wei Li Jianhong Chen Zitao Yu
author_sort	Yifan Wu
title	Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm
title_short	Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm
title_full	Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm
title_fullStr	Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm
title_full_unstemmed	Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm
title_sort	peak-load-regulation nuclear power unit fault diagnosis using thermal sensors combined with improved ica-rf algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/27f762885ddc4bd1a174f97bd1944da0
work_keys_str_mv	AT yifanwu peakloadregulationnuclearpowerunitfaultdiagnosisusingthermalsensorscombinedwithimprovedicarfalgorithm AT kaiyuwu peakloadregulationnuclearpowerunitfaultdiagnosisusingthermalsensorscombinedwithimprovedicarfalgorithm AT weili peakloadregulationnuclearpowerunitfaultdiagnosisusingthermalsensorscombinedwithimprovedicarfalgorithm AT jianhongchen peakloadregulationnuclearpowerunitfaultdiagnosisusingthermalsensorscombinedwithimprovedicarfalgorithm AT zitaoyu peakloadregulationnuclearpowerunitfaultdiagnosisusingthermalsensorscombinedwithimprovedicarfalgorithm
_version_	1718431632678977536

Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm

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