Ensemble Symplectic Singular Mode Decomposition and Its Application in Fault Diagnosis of Rolling Bearings
The analysis of time series is the most important means in the field of mechanical fault diagnosis,but the picked up signals often contain a large amount of interference noise,which seriously affects the accuracy of fault diagnosis.Therefore,a noise reduction method called Ensemble Symplectic Singular Mode Decomposition(ESSMD)is proposed.In this method,mutual information function method and GP algorithm are used to set parameters adaptively,and the symplectic geometric similarity transformation matrices are constructed to obtain denoised component signals.However,the compo-nents obtained by symplectic geometry similarity transformation may be coupled with noise information,which is difficult to be removed through the traditional components filtering.In order to weaken the coupling noise in the component signals,the Lagrange multipliers is further introduced in ESSMD to suppress the interference of noise in the components to pure signal information and obtain a cleaner pure signal matrix.The simulation and experimental results indicate that ESSMD can effec-tively reduce the noise included in the signal.
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