Journal article
Prognosis of electric scooter with intermittent faults: Dual degradation processes approach
IEEE Transactions on Vehicular Technology, Vol.71(2), pp.1411-1425
2022
Abstract
Prognosis of failing components under intermittent faults is challenging since intermittent faults gradually deteriorate in duration and magnitude over time meanwhile show the stochasticity of fault appearance and disappearance. To address the problem, this paper proposes an intelligent prognosis method for intermittently faulty components in electric scooter based on dual degradation processes. Firstly, fault detection and isolation is used to identify discrete faults and isolate possible intermittent faults in continuous components, where the fault isolation performance under multiple faults condition is improved by developing an extended fault signature matrix. Secondly, the adaptive competitive swarm optimization is proposed to identify the magnitude, appearing and disappearing instants of each intermittent fault for faulty components. After that, the dual degradation processes are established with the aid of tumbling window (TW), where the duration degradation process describes the evolutionary trend of the ratio of fault duration to the length of duration-TW, while the magnitude degradation process captures the evolutionary trend of maximum feature in magnitude-TW. With dual degradation processes and predefined failure thresholds, the remaining useful life of the faulty component is jointly predicted, where the degradation speed difference between duration and magnitude is considered. Finally, the proposed methods are validated by experiment results.
Details
- Title
- Prognosis of electric scooter with intermittent faults: Dual degradation processes approach
- Authors/Creators
- C. Xiao (Author/Creator)M. Yu (Author/Creator)H. Wang (Author/Creator)B. Zhang (Author/Creator)D. Wang (Author/Creator)
- Publication Details
- IEEE Transactions on Vehicular Technology, Vol.71(2), pp.1411-1425
- Publisher
- IEEE Xplore
- Identifiers
- 991005544366507891
- Copyright
- © 2021 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
Metrics
49 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, Electrical & Electronic
- Telecommunications
- Transportation Science & Technology
- ESI research areas
- Engineering