Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA - Yang Sen; Wang Hengdi; Cui Yongcun; Li Chang; Tang Yuanchao (Jixie chuandong)
Aiming at the problem that the initial fault signal of rolling bearings is weak and the fault characteristic is difficult to extract, this study proposes a rolling bearing fault diagnosis method based on variational modal decomposition (VMD) for adaptive parameter optimization based on the improved sparrow search algorithm (SSA) and the extreme learning machine (ELM) with multi-layer feature vector fusion. Firstly, the optimization step size of SSA is adaptively changed according to the fittness function value and the number of iterations. Secondly, the improved SSA optimizes the important parameters (decomposition number K and penalty factor α) of the VMD algorithm, and the fittness function adopts the minimum envelope entropy. Thirdly, the intrinsic mode function (IMF) component with the smallest envelope spectral entropy after SSA-VMD decomposition is extracted as the optimal component, and its eigenvalue is calculated. Finally, through the screening of coefficients of the variation method, the root mean square value and peak value are constructed as the two-dimensional eigenvalue vector of the first layer, and the sample entropy, kurtosis and root mean square are constructed as the three-dimensional eigenvalue vector of the second layer, which are respectively sent to the limit learning machine ELM for the training and classification of rolling bearing faults.The experiment results show that the proposed algorithm has good fault diagnosis performance,ultimately achieving a classification accuracy of 98.25% and an actual diagnostic accuracy of 93.36%.