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A novel fault diagnosis method for gearbox based on RVMD and TELM with composite chaotic grey wolf optimizer

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Publicado en:Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 24793
Autor principal: Huang, Xuebin
Otros Autores: Xu, Anfeng, Liu, Hongbing, Ye, Bingcheng
Publicado:
Nature Publishing Group
Materias:
Signal processing
Accuracy
Embedded systems
Wavelet transforms
Fourier transforms
Fault diagnosis
Neural networks
Decomposition
Biomedical engineering
Data analysis
Optimization algorithms
Vibration
Statistical methods
Learning algorithms
Environmental
Acceso en línea:Citation/Abstract
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Resumen:Fault diagnosis for gearbox by robust variational mode decomposition (RVMD) and twin extreme learning machine (TELM) with composite chaotic grey wolf optimizer (CCGWO) is proposed in this study. Robust variational mode decomposition is an advanced signal processing technique designed to decompose complex signals into intrinsic mode functions (IMFs) while maintaining robustness against noise and outliers,which addresses the limitations of variational mode decomposition (VMD), particularly its sensitivity to noise and its tendency to produce suboptimal results in the presence of outliers. The proposed twin extreme learning machine with composite chaotic grey wolf optimizer (CCGTELM) model can extract higher-level features and has higher classification accuracy than traditional ELM. A novel grey wolf optimization algorithm, named composite chaotic grey wolf optimizer (CCGWO), is used to optimize the kernel parameter of TELM. Thus, TELM with CCGWO (DGTELM) is used to fault diagnosis for gearbox.The experimental results demonstrates that fault diagnosis accuracy of RVMD–CCGTELM is higher than VMD-TELM, VMD–DNN, VMD–CNN, VMD–LSTM, EMD–ELM and WT–ANN, and RVMD–CCGTELM is suitable for fault diagnosis of gearbox.
ISSN:2045-2322
DOI:10.1038/s41598-025-08318-2
Fuente:Science Database