Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings

The bearing vibration signal with strong non-stationary properties is normally composed of multiple components (e.g. periodic impulses, background noise and other external signal), where periodic impulses relevant to bearing fault are easily contaminated by background noise and other external signal, making it difficult to excavate the inherent fault features. Variational mode decomposition (VMD) is a recently introduced approach for analyzing multi-component signal and is formulated based on the classic Wiener filter. However, some challenges remain when VMD is employed to process the real non-stationary signal. The most significant aspect of difficulties is that the inside parameters of VMD demand to be set manually in advance. To overcome this weakness, a modified method known as cuckoo search algorithm-based variational mode decomposition (CSA-VMD) is proposed in this paper, which can decompose adaptively a multi-component signal into a superposition of sub-signals termed as intrinsic mode function (IMF) by means of parameter optimization. In addition, a spectrum analysis technique called optimal scale morphological slice bispectrum (OSMSB) is presented for extracting fault symptoms from the susceptive mode components, which can restrain the Gaussian noise to a great extent and enhance the fault characteristics. Finally, a new fault diagnosis scheme consisting of CSA-VMD and OSMSB are compared with the existing methods (e.g. CEEMD-FWEO and fast kurtogram) by the application of simulation signal and experimental data, and the comparative results verify the effectiveness and superiority of the proposed method in extracting bearing outer race fault symptoms. The studies provide a novel perspective for the improvement of bearing outer race fault detection. (C) 2018 Elsevier Ltd. All rights reserved.