基于低秩稀疏分解算法的航空锥齿轮故障诊断

Bevel gears are a key component in aero-engine transmission systems, which always work in harsh environment such as high speed and high load. Therefore, they inevitably suffer performance degradation. However, the observed signals are also contaminated by strong background noises and harmonic interferences. In the work,a novel low rank sparse decomposition method was proposed for aero-engine bevel gear fault diagnosis. Firstly, due to the self-similarity of impulsive feature, an adaptive partition window was designed to transform the impulsive feature into a data matrix. By performing the SVD decomposition, the singular value distribution of feature signal exhibited sparse property, and then the sparse low rank prior of feature signal was established, which was further modeled by nuclear norm. Subsequently, by incorporating the classic sparse learning model and the nuclear norm of feature signal, a novel sparse low rank model was proposed. Furthermore, a proximal gradient based on block coordinate decent solver was also developed. The effectiveness of the proposed model and algorithm were evaluated through performing the diagnosis of aero-engine bevel gears.