Fault diagnosis method for bearings based on shock pulse method and multipoint optimal minimum entropy deconvolution adjusted

The Shock Pulse Method (SPM) has been widely applied in the diagnosis of rolling bearing faults and proven to be an efficient and concise approach. The limitations of the SPM approach, which requires the use of specialized SPM sensors, hinder its development. To address this issue, this paper proposes a fault diagnosis method for rolling bearings by integrating Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) with SPM. The MOMEDA method is employed to identify periodic impulse components in the signal and construct an optimal filter for extraction. Subsequently, the SPM method is applied for quantification and diagnosis of faults in rolling bearings. The effectiveness and superiority of the proposed method are validated through extensive simulations of bearing fault signals and publicly available bearing fault signal datasets.