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Adaptive cuckoo search-extreme learning machine based prognosis for electric scooter system under intermittent fault

Yu, M., Xiao, C., Wang, H.ORCID: 0000-0003-2789-9530, Jiang, W. and Zhu, R. (2021) Adaptive cuckoo search-extreme learning machine based prognosis for electric scooter system under intermittent fault. Actuators, 10 (11). Article 283.

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Abstract

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.

Item Type: Journal Article
Murdoch Affiliation(s): Engineering and Energy
Centre for Water, Energy and Waste
Harry Butler Institute
Publisher: MDPI
Copyright: © 2021 by the authors
URI: http://researchrepository.murdoch.edu.au/id/eprint/62952
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