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Fault Feature-Extraction Method of Aviation Bearing Based on Maximum Correlation Re'nyi Entropy and Phase-Space
Zhen Zhang1, Baoguo Liu2, Yanxu Liu2
1School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China.
Abstract:
To address the difficulty of extracting the features of composite-fault signals under a low signal-to-noise ratio and complex noise conditions, a feature-extraction method based on phase-space reconstruction and maximum correlation Re'nyi entropy deconvolution is proposed. Using the Re'nyi entropy as the performance index, which allows for a favorable trade-off between sporadic noise stability and fault sensitivity, the noise-suppression and decomposition characteristics of singular-value decomposition are fully utilized and integrated into the feature extraction of composite-fault signals by the maximum correlation Re'nyi entropy deconvolution. Verification based on simulation, experimental data, and a bench test proves that the proposed method is superior to the existing methods regarding the extraction of composite-fault signal features.
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