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Published (Version of Record)CC BY V4.0, Open Access
Journal article
Computational intelligence-based prognosis for hybrid mechatronic system using improved Wiener process
Actuators, Vol.10(9), Article 213
2021
Abstract
In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive square root cubature Kalman filter is proposed when the noise distributions are unknown. Then, the improved Wiener process incorporating nonlinear term is developed to build the degradation model of incipient fault based on the fault estimation results. For prognosis, the fast krill herd algorithm is proposed to estimate unknown degradation model coefficients. After that, the probability density function of remaining useful life is derived using the identified degradation model. Finally, the proposed methods are validated by simulations.
Details
- Title
- Computational intelligence-based prognosis for hybrid mechatronic system using improved Wiener process
- Authors/Creators
- M. Yu (Author/Creator)H. Lu (Author/Creator)H. Wang (Author/Creator)C. Xiao (Author/Creator)D. Lan (Author/Creator)J. Chen (Author/Creator)
- Publication Details
- Actuators, Vol.10(9), Article 213
- Publisher
- MDPI
- Identifiers
- 991005540006407891
- Copyright
- © 2021 by the authors
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Journal article
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- 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