Lili Bai1,*, Wenhui Li1, He Ren1,2, Feng Li1, Tao Yan1, Lirong Chen3
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4513-4531, 2024, DOI:10.32604/cmc.2024.051348
Abstract Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery, where weak fault characteristic signals hinder accurate fault state representation, we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform (FAWT) with Nonlinear Quantum Permutation Entropy. FAWT, leveraging fractional orders and arbitrary scaling and translation factors, exhibits superior translational invariance and adjustable fundamental oscillatory characteristics. This flexibility enables FAWT to provide well-suited wavelet shapes, effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults. In our approach,… More >
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