Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault

In this paper, an adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method is developed for an electric scooter system with intermittent faults. Firstly, bond-graph-based fault detection and isolation is carried out to find possible faulty components in the electric scooter s...

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Autores principales:	Ming Yu, Chenyu Xiao, Hai Wang, Wuhua Jiang, Rensheng Zhu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	intermittent fault distributed fault estimation adaptive Cuckoo search-extreme learning machine remaining useful life prediction Materials of engineering and construction. Mechanics of materials TA401-492 Production of electric energy or power. Powerplants. Central stations TK1001-1841
Acceso en línea:	https://doaj.org/article/74a315d4b011495489ce1ac4b9302205
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spelling	oai:doaj.org-article:74a315d4b011495489ce1ac4b93022052021-11-25T15:56:48ZAdaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault10.3390/act101102832076-0825https://doaj.org/article/74a315d4b011495489ce1ac4b93022052021-10-01T00:00:00Zhttps://www.mdpi.com/2076-0825/10/11/283https://doaj.org/toc/2076-0825In this paper, an adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method is developed for an electric scooter system with intermittent faults. Firstly, bond-graph-based fault detection and isolation is carried out to find possible faulty components in the electric scooter system. Secondly, submodels are decomposed from the global model using structural model decomposition, followed by adaptive Cuckoo search (ACS)-based distributed fault estimation with less computational burden. Then, as the intermittent fault gradually deteriorates in magnitude, and possesses the characteristics of discontinuity and stochasticity, a set of fault features that can describe the intermittent fault’s evolutionary trend are captured with the aid of tumbling window. With the obtained dataset, which represents the fault features, the ACS-ELM is developed to model the intermittent fault degradation trend and predict the remaining useful life of the intermittently faulty component when the physical degradation model is unavailable. In the ACS-ELM, the ACS is employed to optimize the input weights and hidden layer biases of an extreme learning machine, to improve the algorithm performance. Finally, the proposed methodologies are validated by a series of simulation and experiment results based on the electric scooter system.Ming YuChenyu XiaoHai WangWuhua JiangRensheng ZhuMDPI AGarticleintermittent faultdistributed fault estimationadaptive Cuckoo search-extreme learning machineremaining useful life predictionMaterials of engineering and construction. Mechanics of materialsTA401-492Production of electric energy or power. Powerplants. Central stationsTK1001-1841ENActuators, Vol 10, Iss 283, p 283 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intermittent fault distributed fault estimation adaptive Cuckoo search-extreme learning machine remaining useful life prediction Materials of engineering and construction. Mechanics of materials TA401-492 Production of electric energy or power. Powerplants. Central stations TK1001-1841
spellingShingle	intermittent fault distributed fault estimation adaptive Cuckoo search-extreme learning machine remaining useful life prediction Materials of engineering and construction. Mechanics of materials TA401-492 Production of electric energy or power. Powerplants. Central stations TK1001-1841 Ming Yu Chenyu Xiao Hai Wang Wuhua Jiang Rensheng Zhu Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault
description	In this paper, an adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method is developed for an electric scooter system with intermittent faults. Firstly, bond-graph-based fault detection and isolation is carried out to find possible faulty components in the electric scooter system. Secondly, submodels are decomposed from the global model using structural model decomposition, followed by adaptive Cuckoo search (ACS)-based distributed fault estimation with less computational burden. Then, as the intermittent fault gradually deteriorates in magnitude, and possesses the characteristics of discontinuity and stochasticity, a set of fault features that can describe the intermittent fault’s evolutionary trend are captured with the aid of tumbling window. With the obtained dataset, which represents the fault features, the ACS-ELM is developed to model the intermittent fault degradation trend and predict the remaining useful life of the intermittently faulty component when the physical degradation model is unavailable. In the ACS-ELM, the ACS is employed to optimize the input weights and hidden layer biases of an extreme learning machine, to improve the algorithm performance. Finally, the proposed methodologies are validated by a series of simulation and experiment results based on the electric scooter system.
format	article
author	Ming Yu Chenyu Xiao Hai Wang Wuhua Jiang Rensheng Zhu
author_facet	Ming Yu Chenyu Xiao Hai Wang Wuhua Jiang Rensheng Zhu
author_sort	Ming Yu
title	Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault
title_short	Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault
title_full	Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault
title_fullStr	Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault
title_full_unstemmed	Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault
title_sort	adaptive cuckoo search-extreme learning machine based prognosis for electric scooter system under intermittent fault
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/74a315d4b011495489ce1ac4b9302205
work_keys_str_mv	AT mingyu adaptivecuckoosearchextremelearningmachinebasedprognosisforelectricscootersystemunderintermittentfault AT chenyuxiao adaptivecuckoosearchextremelearningmachinebasedprognosisforelectricscootersystemunderintermittentfault AT haiwang adaptivecuckoosearchextremelearningmachinebasedprognosisforelectricscootersystemunderintermittentfault AT wuhuajiang adaptivecuckoosearchextremelearningmachinebasedprognosisforelectricscootersystemunderintermittentfault AT renshengzhu adaptivecuckoosearchextremelearningmachinebasedprognosisforelectricscootersystemunderintermittentfault
_version_	1718413386422681600

Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault

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