Sparse decomposition based aero-engine's bearing fault diagnosis

Aero-engine's rolling bearings work in harsh environment such as high temperature, high speed and sharply varying load, thus resulting in complex failure modes and diagnostic inefficient. A novel fault feature extraction technique based on sparse decomposition theory, stagewise matching morphological component analysis (SMMCA) is proposed to decompose compound multicomponent signal into its building blocks which contain the fault feature information. The sparse decomposition method can decompose a signal in a redundant dictionary and obtain the sparse representation of the signal by an optimization algorithm. In the spirit of the sparse decomposition theory, the SMMCA firstly constructs specialized dictionaries where the distinct components of the original signal can be represented sparsely, and then the coefficients of each subcomponent in the tailed dictionary are obtained through the stagewise orthogonal matching pursuit (StOMP) algorithm, lastly, the subcomponent is reconstructed by virtue of the specialized dictionary and the corresponding coefficients. With the proposed method, the vibration signal of the aero engine fault bearing can be diagnosed precisely. The effectiveness of the proposed method is verified by both the simulation and the experiment.