De-Noising Method Based on Dual-Tree Complex Wavelet Packet Transform and Principal Manifold and its Application for Fault Diagnosis

摘要

In order to extract the week fault features contained in the vibration signal of the mechanical equipment, a new de-nosing method based on dual-tree complex wavelet packet transform (DTCWPT) and principal manifold was proposed. Firstly, the vibration signals were decomposed into several sub-frequency bands by DTCWPT, the Shannon entropy was used to seek the best basis of DTCWPT, and a new adaptive threshold function was employed to denoise the wavelet packet coefficients on the best basis of the real part and imaginary part of DTCWPT via the de-noising criterion, then the wavelet packet coefficients were reconstructed into a high dimensional space. Secondly, t-distributed stochastic neighbor embedding (t-SNE) was performed to extract a low dimensional manifold, the proposed threshold function was further applied to process the low dimensional manifold, aiming at separate the signal and noise, the principal manifold was reconstructed by the method of spectral regression analysis, thus, the signals were reconstructed back into one dimensional time series after eliminating the noise. Finally, a simulated signal and a real bearing fault signal were used to validate the proposed method, the results have demonstrated that the proposed method has a good performance of nonlinear noise reduction, and can extract fault features of rolling bearing effectively.