Period-adaptive minimum noise amplitude deconvolution and its application in bearing fault diagnosis

Minimum noise amplitude deconvolution (MNAD) has shown considerable potential for enhancing impulsive signatures in rolling bearing fault diagnosis. Nevertheless, its effectiveness is highly sensitive to the selection of prior parameters, particularly the fault period. In engineering practice, the parameter is commonly obtained either from manual calculations based on shaft speed and bearing geometry or from empirical experience, which restricts the adaptability and automation of the method. To overcome this limitation, a fault-period-adaptive minimum noise amplitude deconvolution (FPA-MNAD) approach is proposed in this study. Firstly, a multiscale envelope spectrum dominant peak detection (MESDPD) scheme is proposed. Based on the MESDPD, the fault characteristic frequency is determined and further transformed into the corresponding fault period, avoiding manual derivation and subjective threshold selection. The estimated fault period is then integrated into the MNAD procedure, allowing adaptive updating and optimization of the filter period parameter. In this way, weak repetitive transients and their periodic behavior can be extracted more effectively in heavy noise environments. Experimental investigations demonstrate that the proposed method provides impressive fault feature enhancement and improved diagnostic robustness, while reducing reliance on prior information and operator intervention. These results indicate that the method has strong potential for practical engineering applications.