Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
Multi-channel signal has more abundant and accurate state characteristic information than single channel signal. How to separate fault characteristic information from the multi-channel signal is the key of fault diagnosis. As two typical multi-channel signal decomposition methods, multivariate empir...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1789dd9e615f4b749521c74e800dc5b3 |
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| Sumario: | Multi-channel signal has more abundant and accurate state characteristic information than single channel signal. How to separate fault characteristic information from the multi-channel signal is the key of fault diagnosis. As two typical multi-channel signal decomposition methods, multivariate empirical mode decomposition (MEMD) and multivariate variational mode decomposition (MVMD) are widely used in multi-channel signal analysis. However, MEMD and MVMD use cyclic iteration to complete the analysis of multi-channel signals, and it is difficult to overcome their inherent defects. In view of this, based on nonlinear sparse mode decomposition (NSMD), this paper proposes a multivariate nonlinear sparse mode decomposition (MNSMD) by constraining singular local linear operators to separate the natural oscillation modes in multi-channel signal. By constraining singular local linear operators into signal decomposition, MNSMD has obvious advantages in restraining mode aliasing and robustness. In addition, the local narrow-band component is used as the basis function for iteration, and the component signal is obtained by approaching the original signal. Through the simulation signal and gear fault signal analysis, the results show that, compared with MEMD and MVMD methods, MNSMD method can effectively complete gear fault diagnosis. |
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