An extended iterative filtering and composite multiscale fractional-order Boltzmann-Shannon interaction entropy for rolling bearing fault diagnosis

Feature extraction remains a challenging task in bearing fault diagnosis due to the presence of nonlinearity, nonstationarity, and noise interference. To address this issue, an extended iterative filtering and composite multiscale fractional-order Boltzmann-Shannon interaction entropy (EIF-CMFBSIE) are proposed for for rolling bearing fault diagnosis in complex environments. First, an EIF method is proposed to decompose the vibration signal into multiple intrinsic mode functions (IMFs) by extending the lengths of both ends of the signal through waveform matching. Second, multi-scale coarse-graining is applied to each IMF, fractional-order Boltzmann-Shannon interaction entropy (FBISE) is computed for each coarse-grained sequence by incorporating fractional-order parameters, and CMFBSIE is obtained through composite averaging to construct a multi-dimensional fault feature set. Next, the joint approximate diagonalization of eigenmatrices (JADE) method is employed to eliminate redundant information and fuse the fault features. The fused feature sets are then input into the kernel extreme learning machine (KELM) classifier for multi-fault identification. The proposed EIF-CMFBSIE method demonstrates excellent performance in analyzing the nonlinear dynamic complexity and irregularity of vibration signals in noisy environments. In the fault diagnosis tests based on three bearing simulation test benches, compared with the existing five fault diagnosis methods, the recognition accuracy of EIF-CMFBSIE is increased by 13.33%, and there is a significant advantage in computational efficiency, in which the EIF shortens the decomposition time by 65–96% compared with the existing methods. The experimental results indicate that the method can not only accurately identify different fault types and the degree of faults, but also has a short calculation time and better overall performance.