Find in Library

Fault diagnosis of rotating machinery plays an important role for the reliability and safety of modern industrial systems, and it is challenging to detect the weak character signal in the noisy background. This article presents an improved resonance sparse decomposition method for fault diagnosis of rolling bearings. The PSO algorithm is used to optimise the quality factor of the resonance sparse decomposition, which can overcome the deficiency caused by the manual given quality factor in the traditional resonance sparse decomposition method and achieve the effective separation of resonance components. An envelope analysis of the low-resonance decomposition including the major fault components was performed to extract the fault feature frequency. Based on this, the two decomposition methods are applied to the fault diagnosis of the inner and outer rings of the bearing, respectively. The experimental results show that the improved resonance sparse decomposition method can reduce the interference components when extracting the frequency of fault features and improve the accuracy of fault diagnosis.