Adaptive variational mode decomposition based on Archimedes optimization algorithm and its application to bearing fault diagnosis. (15th March 2022)

• Record Type: Journal Article
• Authors: Wang, Junxiang; Zhan, Changshu; Li, Sanping; Zhao, Qiancheng; Liu, Jiuqing; Xie, Zhijie
• Is Part Of: Measurement. Volume 191(2022)
• Publication Date: 2022-03-15
• Subjects: Variational mode decomposition -- Fault diagnosis -- Signal processing -- Archimedes optimization algorithm; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.110798
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A novel index is proposed to evaluate the signal complexity. The envelope signal is Fourier transformed twice in the objective function. A novel adaptive VMD method based on the AOA is proposed. Abstract: Variational mode decomposition (VMD) is widely used in rotating machinery fault diagnosis. However, the choice of its main parameters is often based on experience, affecting the decomposition results. Aiming to mitigate this drawback, an adaptive VMD method using the Archimedes optimization algorithm (AOA) is presented. Firstly, the computational domain of the objective function is set to the amplitude spectrum of the signal envelope spectrum. Secondly, a correlation waveform index (Cwi) is proposed to evaluate the complexity of the signal. The minimum average value of the Cwi of all intrinsic modal functions (IMFs) is taken as the objective function. Finally, the AOA is used to search for the optimal mode number and penalty factor to find IMFs which are sensitive to fault features. Compared to the other improved VMD methods, the proposed method has a better performance in extracting the fault characteristics from the simulated and actual cases.