A VME method based on the convergent tendency of VMD and its application in multi-fault diagnosis of rolling bearings. (July 2022)

• Record Type: Journal Article
• Authors: Li, Cuixing; Liu, Yongqiang; Liao, Yingying; Wang, Jiujian
• Is Part Of: Measurement. Volume 198(2022)
• Publication Date: 2022-07
• Subjects: VME -- Fault diagnosis -- VMD convergence tendency chart -- Center frequency -- Balance factor; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111360
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The novel VME method is applied to bearing multi-fault diagnosis. The influence of the balance factor on the convergence characteristic of VMD is analyzed. VMD convergence tendency chart is completely data-driven, which can quickly determine the ICFs of all latent modes in the signal. The VMD convergence tendency chart is used to determine the key parameter of VME, which improves the adaptability of VME. Abstract: Variational mode extraction (VME) is a novel fault diagnosis technology, which can effectively extract a narrow-band mode from the multi-component signal. However, its performance is seriously affected by two initial parameters: the initial guess of center frequency ω d and the balance factor α . To determine these two parameters adaptively, a VME method based on the convergence property of variational mode decomposition (VMD) is proposed for the multi-fault diagnosis of rolling bearings. Firstly, we study the influence of the balance factor on the convergence characteristic of VMD, the VMD convergence tendency chart is established. Secondly, the initial center frequencies of all latent modes in the faulty signal are adaptively determined by the VMD convergence tendency chart; taking kurtosis as the evaluation index, the corresponding balance factor is optimized separately. Finally, VME is used to extract all modes one by one. The experimental analog signal and engineering application signal are analyzed by this new method and compared with ensemble empirical mode decomposition (EEMD), fast kurtogram, and single-mode VMD. The results display that the new method can accurately extract multiple fault features from the bearing compound fault signal, and the fault feature extraction accuracy is better than the above three classical signal processing methods.