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Title: Prognosis of electric scooter with intermittent faults: dual degradation processes approach
Authors: Xiao, Chenyu
Yu, Ming
Wang, Hai
Zhang, Bin
Wang, Danwei
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Xiao, C., Yu, M., Wang, H., Zhang, B. & Wang, D. (2021). Prognosis of electric scooter with intermittent faults: dual degradation processes approach. IEEE Transactions On Vehicular Technology, 71(2), 1411-1425.
Journal: IEEE Transactions on Vehicular Technology
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.
ISSN: 0018-9545
DOI: 10.1109/TVT.2021.3131998
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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