Research on Mechanical Equipment Fault Diagnosis Based on PA-VME and SPP
Aiming at the low signal quality and poor diagnosis accuracy in traditional fault identification methods,this paper proposes a new data-driven mechanical fault diagnosis method combining Parameter Adaptive Variational Mode Extraction(PA-VME)and Sparse Preserving Projection(SPP).A new index LFCI is constructed by combining the correlation coefficient,L-kurtosis and information entropy as a fitness function.Particle Swarm Optimization algorithm is used to optimize the internal parameters of VME,so as to form a novel PA-VME model and use it for the mode decomposition of vibration signals.According to the principle that the constructed index can reflect the order of information,the interested model components are selected and the high-dimensional feature data set is calculated.SPP is applied to project the data set into the low dimensional space through the weight matrix to achieve dimension reduction and clustering analysis of high-dimensional feature data.The analysis of simulation signals and test-bed fault signals proves that the recognition accuracy of the proposed model for different faults can reach 96.87％.
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