An iterative morphological difference product wavelet for weak fault feature extraction in rolling bearing fault diagnosis

Weak fault feature extraction is of great significance to the diagnosis rolling bearing. At early stage defects, features are usually weak and easily submerged in strong background noise, which makes information extremely difficult be excavated. This paper proposes an iterative morphological difference product wavelet (MDPW) address this issue. In scheme, firstly, filter (MDPF) developed using combination filter-hat transform operator operator. The MDPF then incorporated into a undecimated construct MDPW, can achieve noise suppression enhancement. Subsequently, optimal iteration numbers that influence performance MDPW determined severity indicator, effectively extracts periodic impulse related failure Finally, identification inferred by occurrence defect frequencies spectrum with numbers. validity evaluated through numerical simulations experiment cases. analysis results demonstrate has higher accuracy than existing algorithms (e.g., adaptive single-scale weighted multi-scale wavelet). research provides new perspective for improving