Abstract
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.
Highlights
Gear is the most vulnerable part of rotating machinery and equipment, and its state will affect the healthy operation of the entire machinery [1]
Compared with multivariate empirical mode decomposition (MEMD) and multivariate variational mode decomposition (MVMD), multivariate nonlinear sparse mode decomposition (MNSMD) is an effective multi-channel signal decomposition method, which provides a reference for gear fault diagnosis
In view of the shortcomings of the existing multichannel analysis methods, A multivariate nonlinear sparse mode decomposition (MNSMD) method is proposed by constraining singular local linear operators
Summary
Gear is the most vulnerable part of rotating machinery and equipment, and its state will affect the healthy operation of the entire machinery [1]. Cao et al proposed a multi-channel signal denoising method based on MVMD [22], which uses the subspace projection of multivariate variational decomposition to complete the noise reduction of multi-channel signal. Aiming at the limitation of MEMD and MVMD methods in multi-channel signal analysis, this paper proposes a multivariable nonlinear sparse mode decomposition (MNSMD) based on nonlinear sparse mode decomposition (NSMD). In MNSMD, by constraining singular local linear operators into signal decomposition, MNSMD can adaptively decompose a complex signal into several local narrowband components with physical significance of instantaneous frequency, and has obvious advantages in restraining mode aliasing and robustness [24]. MNSMD method, like MEMD and MVMD methods, can decompose the complex multi-channel signal into the sum of components adaptively.
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