Bearing fault diagnosis model using improved Bayesian information criterion-based variational modal decomposition and IGA-SVM

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In rotating machinery, the bearing vibration signal is easily covered by the vibration signal of other components, which makes the fault characteristic signal not obvious in the collected vibration signal. In order to better separate the vibration source from the collected vibration signal, a variational modal decomposition optimized by bayesian information criterion (VMDBIC) is proposed. At the same time, the data set is constructed based on the separated vibration source signals, and the support vector machine (SVM) optimized by improved genetic algorithm (IGA) is used to diagnose the bearing fault. Firstly, the Bayesian information criterion is used to calculate the number of potential vibration sources in the vibration signal. The calculated number of vibration sources is used as the number of modal components of variational modal decomposition (VMD) to decompose vibration signal. Secondly, the modal components obtained by VMD are used to construct the eigenmatrix of vibration signal. And the singular value energy spectrum of 3 bearing types (a total of 24 fault types) is calculated based on the eigenmatrix of vibration signal. Finally, the singular value energy spectrum is used as the input of IGA-SVM model to diagnose the bearing fault. The effectiveness of VMDBIC method is verified from three aspects of the time-frequency domain signal, the index of orthogonality (IO), and the index of energy conservation (IEC). The feasibility of the method proposed in this paper is verified from three aspects: fault identification effect of various types of bearings, load changes, and mutual diagnosis of different bearings.