Multi-model optimization algorithm based on signal feature parameters and its application in fault diagnosis

To achieve accurate signal analysis, this paper designs a rule for constructing a multi-model fitness function by integrating fault feature ratio (FFR), index of orthogonality (IO), index of energy conservation (IEC), envelope entropy, and kurtosis. Based on this rule, a vibration signal analysis method named multi-objective optimization algorithm-based variational mode decomposition with Bayesian Information Criterion (MOA–VMD–BIC) is proposed. First, Model_A ’s fitness function is constructed using envelope entropy and kurtosis, and an optimization algorithm is employed to obtain the optimal VMD parameters { k , α}. Simultaneously, the values of Model_B (built with FFR, IO, and IEC) are calculated. Second, the intersection of solutions from Model_A and Model_B is derived to determine the optimal parameters { k , α} for the multi-objective optimization framework. Next, the optimal parameters are applied to decompose the original signal X into intrinsic mode functions (IMFs) via VMD. A signal sequence is then constructed using X and its components, and the Bayesian Information Criterion (BIC) is utilized to estimate the number of vibration sources in X . Finally, the reconstructed vibration source signals are subjected to fast Fourier transform (FFT) and Hilbert envelope analysis to diagnose mechanical faults. Simulation signals and experimental datasets validate the effectiveness of the proposed method.