Weak fault feature extraction of rolling bearing based on IHBA-SVMD-MCKD and Dynamic Threshold via Pearson and Kurtosis

Abstract Extracting weak fault features under strong background noise interference is key to early fault diagnosis of rolling bearings. A method combining the improved honey badger algorithm (IHBA), successive variational mode decomposition (SVMD), and maximum correlated kurtosis deconvolution (MCKD) is proposed. The SVMD method effectively suppresses noise while retaining key fault features. To improve feature extraction, IHBA is used as the optimization tool, and a composite index PE composed of the Pearson correlation coefficient ratio-envelope spectrum peak factor is used as the fitness function, enhancing the efficiency and stability of parameter optimization. To address the issue of incorrect IMF component selection leading to excessive noise and poor robustness, the dynamic threshold via Pearson and kurtosis is constructed, which adaptively selects IMF components rich in fault features for signal reconstruction. MCKD can highlight continuous pulse signals obscured by noise. IHBA is used again, with a composite index EE composed of envelope entropy-envelope spectrum peak factor as the fitness function, and the SVMD-processed signal is input to MCKD. Finally, envelope demodulation is used to extract rolling bearing fault feature frequencies, significantly improving the accuracy of weak fault feature extraction. Through verification with simulated signals of early rolling bearing faults and the full-lifecycle dataset XJTU-SY, the proposed method improves the accuracy and robustness of early fault diagnosis under strong background noise and other interference factors, providing a possible solution for extracting weak fault features.