周 期 精 炼 的 最 大 相 关 峭 度 解 卷 积 在 滚 动 轴 承微 弱 故 障 特 征 提 取 中 的 应 用

Maximum correlated kurtosis deconvolution (MCKD), which uses correlated kurtosis as its deconvolution target, effectively extracts both periodic and impulsive features of mechanical faults. This is a widely used method for solving rolling bearing fault diagnosis problems. However, the performance of MCKD heavily relies on accurate prior fault period information. Existing solution often only focus on period estimation during the iterative process, making them ineffective under low signal-to-noise ration (SNR) conditions. To address this limitation, a period-refined maximum corrlated kurtosis deconvolution (PRMCKD) method is proposed. This approach refines the iteration period using time synchronous averaging (TSA) for reconolution, enabling accurate extraction of subtle bearing fault features even in strong noise environments. The method operates by first utilizing a filter bank for preliminary localization of the resonance frequency band, thus defining the correct deconvolution direction. With correlated kurtosis as the objective function, and leveraging the period information refined by TSA technology, the optimal filter coefficients are iteratively solved. Rolling bearing fault localization is achieved through the fault features present in the filtered signal. Simulation and experimental analysis results demonstrate that the proposed PRMCKD method offers significant advantages over traditional deconvolution methods for extracting weak fault features in rolling bearings.