Adaptive variational mode decomposition based on Archimedes optimization algorithm and its application to bearing fault diagnosis

•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. Variational mode decomposition (VMD) is widely used in rotating machinery fault diagnosis. However, the...

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Vydáno v:Measurement : journal of the International Measurement Confederation Ročník 191; s. 110798
Hlavní autoři: Wang, Junxiang, Zhan, Changshu, Li, Sanping, Zhao, Qiancheng, Liu, Jiuqing, Xie, Zhijie
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Elsevier Ltd 15.03.2022
Elsevier Science Ltd
Témata:
Algorithms
Archimedes optimization algorithm
Bearings
Decomposition
Fault diagnosis
Optimization
Optimization algorithms
Rotating machinery
Signal processing
Variational mode decomposition
Waveforms
Fault diagnosis
Signal processing
Variational mode decomposition
Archimedes optimization algorithm
ISSN:0263-2241, 1873-412X
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Shrnutí:•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. 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.
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ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2022.110798