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• W4229023305 abstract "Early fault detection of rotor systems with rub-impact is a very important and challenging task in practical applications, especially for rub-impact faults in the initial stage. In this paper, the rotor unbalance is taken as the control parameter to yield incipient rub-impact faults, and an early detection scheme for rub-impact faults is proposed based on deterministic learning. A large enough dynamical pattern bank including health (without rub-impact), sub-health (rub-impact inception), fault (rub-impact fault developing) is established based on the underlying system dynamics which are accurately identified through deterministic learning. Compared with the time–frequency analysis methods, the early detection of rub-impact faults is implemented from the viewpoint of accurate identification of system dynamics, which can provide a more effective detection. The differences between the stored dynamical patterns with that of a test system are directly reflected in system residuals, and the early weak rub-impact faults can be detected quickly based on the comparison of the system residuals. Furthermore, the degree of rub-impact faults is also determined accurately. The results of numerical simulation and experiments show the effectiveness of the proposed early rub-impact fault detection method." @default.
• W4229023305 created "2022-05-08" @default.
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• W4229023305 date "2022-07-01" @default.
• W4229023305 modified "2023-09-26" @default.
• W4229023305 title "Early rub-impact fault detection of rotor systems via deterministic learning" @default.
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• W4229023305 doi "https://doi.org/10.1016/j.conengprac.2022.105190" @default.
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