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• W4361009667 startingPage "1548" @default.
• W4361009667 abstract "As the core equipment of wind turbines, rolling bearings affect the normal operation of wind power generators, resulting in huge economic losses and significant social impacts in the case of faults. Most faults are not easily found because of the small vibration response of these rolling bearings that operate in harsh conditions. To address the problem that the fault identifications of rolling bearings are disturbed by the strong noise in wind power generators, an adaptive nonlinear method based on a piecewise hybrid stochastic resonance system with a novel cross-correlation spectral kurtosis is proposed. Then, the vibration signals collected from the fault point of the outer and inner rings are used to clarify the outstanding capability of the proposed method when compared with the maximum cross-correlation-kurtosis-based unsaturated stochastic resonance method. Furthermore, the machine learning method based on the medium tree was adopted to further prove the excellent performance of the piecewise hybrid stochastic resonance system with a novel cross-correlation spectral kurtosis for realizing the efficient detection of rolling bearing faults in wind power generators, which has important innovation significance and practical engineering value for ensuring the safe and stable operation of wind turbines." @default.
• W4361009667 created "2023-03-30" @default.
• W4361009667 creator A5001894134 @default.
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• W4361009667 date "2023-03-25" @default.
• W4361009667 modified "2023-10-18" @default.
• W4361009667 title "Piecewise Hybrid System with Cross-Correlation Spectral Kurtosis for Fault Diagnosis in Rolling Bearing of Wind Power Generator" @default.
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• W4361009667 doi "https://doi.org/10.3390/electronics12071548" @default.
• W4361009667 hasPublicationYear "2023" @default.
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