Modeling and prediction for diesel performance based on deep neural network combined with virtual sample

Abstract The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accu...

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Autores principales:	Hainan Zheng, Honggen Zhou, Chao Kang, Zan Liu, Zhenhuan Dou, Jinfeng Liu, Bingqiang Li, Yu Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d275ae58e97a4005abecec06d1ef59a2
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spelling	oai:doaj.org-article:d275ae58e97a4005abecec06d1ef59a22021-12-02T18:51:52ZModeling and prediction for diesel performance based on deep neural network combined with virtual sample10.1038/s41598-021-96259-x2045-2322https://doaj.org/article/d275ae58e97a4005abecec06d1ef59a22021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96259-xhttps://doaj.org/toc/2045-2322Abstract The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.Hainan ZhengHonggen ZhouChao KangZan LiuZhenhuan DouJinfeng LiuBingqiang LiYu ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hainan Zheng Honggen Zhou Chao Kang Zan Liu Zhenhuan Dou Jinfeng Liu Bingqiang Li Yu Chen Modeling and prediction for diesel performance based on deep neural network combined with virtual sample
description	Abstract The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.
format	article
author	Hainan Zheng Honggen Zhou Chao Kang Zan Liu Zhenhuan Dou Jinfeng Liu Bingqiang Li Yu Chen
author_facet	Hainan Zheng Honggen Zhou Chao Kang Zan Liu Zhenhuan Dou Jinfeng Liu Bingqiang Li Yu Chen
author_sort	Hainan Zheng
title	Modeling and prediction for diesel performance based on deep neural network combined with virtual sample
title_short	Modeling and prediction for diesel performance based on deep neural network combined with virtual sample
title_full	Modeling and prediction for diesel performance based on deep neural network combined with virtual sample
title_fullStr	Modeling and prediction for diesel performance based on deep neural network combined with virtual sample
title_full_unstemmed	Modeling and prediction for diesel performance based on deep neural network combined with virtual sample
title_sort	modeling and prediction for diesel performance based on deep neural network combined with virtual sample
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d275ae58e97a4005abecec06d1ef59a2
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_version_	1718377413887393792

Modeling and prediction for diesel performance based on deep neural network combined with virtual sample

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