Modulation signal bispectrum with optimized wavelet packet denoising for rolling bearing fault diagnosis

Transient impulses caused by local faults are critical informative indicators for rolling element bearing fault diagnosis. The methods accurately extracting transient while suppressing strong background noise and interference components have received extensive studies. In this article, a novel diagnosis scheme based on optimized wavelet packet denoising modulation signal bispectrum is proposed, which takes advantage of the impulse enhancement demodulation ability to diagnose more accurately. First, measured signals decomposed into series time–frequency subspaces using transform. An optimal threshold value selected proposed criterion considering unbiased autocorrelation envelope Gini index impulses. Subsequently, denoised with value, master that containing fault-related indicator. Finally, utilized further purify extract contained in impulses, suboptimal slices characteristic frequency intensity coefficient. detector then obtained averaging determine type faults. denoising-modulation validated simulation experimental Compared variational mode decomposition Teager energy operator, fast kurtogram as well conventional bispectrum, method has superior performance feature incipient defects different components.