Files and links (2)
Published (Version of Record)CC BY V4.0, Open Access
Journal article
Adaptive cuckoo search-extreme learning machine based prognosis for electric scooter system under intermittent fault
Actuators, Vol.10(11), Article 283
2021
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.
Details
- Title
- Adaptive cuckoo search-extreme learning machine based prognosis for electric scooter system under intermittent fault
- Authors/Creators
- M. Yu (Author/Creator)C. Xiao (Author/Creator)H. Wang (Author/Creator)W. Jiang (Author/Creator)R. Zhu (Author/Creator)
- Publication Details
- Actuators, Vol.10(11), Article 283
- Publisher
- MDPI
- Identifiers
- 991005543145407891
- Copyright
- © 2021 by the authors
- Murdoch Affiliation
- School of Engineering and Energy; Harry Butler Institute; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
Metrics
11 File views/ downloads
90 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 7 Engineering & Materials Science
- 7.215 Friction & Vibration
- 7.215.818 Rotating Machinery Diagnostics
- Web Of Science research areas
- Engineering, Mechanical
- Instruments & Instrumentation
- ESI research areas
- Engineering