The vibration signals of ship rolling bearings are prone to being submerged in noise due to the complex environment of the engine room and the effects of periodic and non-periodic impacts on the bearings,making the extraction of fault characteristic frequencies challenging.To address this issue,a novel denoising method combining an complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and independent component analysis(ICA)is proposed for the vibration signals of rolling bearings.This approach specifically aims to improve upon the modal mixing issue inherent in impirical mode decomposition(EMD)derived algorithms by incorporating ICA processing.It utilizes power spectral entropy(PSE)to filter and reconstruct the ICA separated signals.The characteristic frequencies are then extracted through envelope spectrum analysis and fast Fourier transform(FFT)processing of the signals.Application of this method to the signals of rolling bearings with multiple faults has demonstrated a significant reduction in noise and interference.Various parameters have shown good performance,effectively extracting the fault characteristics.
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