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• W2786332815 abstract "As the rolling bearing vibration signal is of nonlinear and nonstationary characteristics, the condition-indicating information distributed in the rolling bearing vibration signal is complicated, and using traditional time domain and frequency domain approaches cannot easily make an accurate estimation for the rolling bearing health status. In this paper, a simple and efficient fault diagnostic approach was proposed to accommodate to the requirements of both real-time monitoring and accurate estimation of fault type as well as severity. Firstly, a four-dimensional feature extraction algorithm using entropy and Holder coefficient theories was developed to extract the characteristic vector from the vibration signals, and secondly a gray relation algorithm was employed for achieving bearing fault pattern recognition intelligently. The experimental study have illustrated the proposed approach can efficiently and effectively improve the fault diagnostic performance compared with the existing artificial intelligent methods, and can be suitable for on-line health status estimation." @default.
• W2786332815 created "2018-02-23" @default.
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• W2786332815 date "2018-01-01" @default.
• W2786332815 modified "2023-10-06" @default.
• W2786332815 title "Study on Rolling Bearing On-Line Health Status Estimation Approach Based on Vibration Signals" @default.
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• W2786332815 doi "https://doi.org/10.1007/978-3-319-73317-3_15" @default.
• W2786332815 hasPublicationYear "2018" @default.
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