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

• Published in: Mechanical systems and signal processing Vol. 122; pp. 56 - 86
• Main Authors: Yan, Xiaoan, Jia, Minping
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.05.2019; Elsevier BV
• Subjects: Background noise; Bearing races; Bearing strength; Computer simulation; Cuckoo search algorithm; Decomposition; Fault detection; Fault diagnosis; Impulses; Morphology; Noise; Optimal scale morphological slice bispectrum; Optimization; Parameters; Random noise; Roller bearings; Rolling bearing; Search algorithms; Signal processing; Signs and symptoms; Spectrum analysis; Superposition (mathematics); Variational mode decomposition; Wiener filtering; Variational mode decomposition; Cuckoo search algorithm; Fault detection; Rolling bearing; Optimal scale morphological slice bispectrum
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2018.12.022

•A modified method called CSA-VMD is proposed for the purpose of noise reduction.•OSMSB is presented for removing stochastic noise and highlight fault characteristics.•A new fault diagnosis scheme consisting of CSA-VMD and OSMSB is formulated.•Simulated and experimental examples proved the effectiveness of the proposed method. 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.