New method for fault diagnosis of rolling bearings based on morphological component analysis

Based on the improvement of the threshold denoising method of morphological component analysis (MCA), a new method for fault diagnosis of rolling bearings based on MCA was proposed. According to the morphological difference of each component, different sparse dictionaries were built with MCA to separate each component from a signal. When a rolling bearing was locally damaged, its vibration signal was often composed of harmonic components with system characteristics of the rolling bearing, impulse components with fault information and random noise. The harmonic components represented the smooth part of the vibration signal, while the impulse components represented the detail part of the vibration signal, therefore, the two kinds of components could be separated according to the morphological difference. The harmonic components, impulse components and random noise component were separated from the vibration signal of a fault rolling bearing by using MCA, and the fault diagnosis of rolling bearing was carried out according to the time interval of impulses in the impulse components. The simulation and application examples proved that the proposed method is effective in extracting the fault impulse components from the vibration signal of a locally damaged rolling bearing.