中文摘要：

航空发动机轴承在高温、高压、高转速等恶劣条件下运行，机动工况较多，因而故障模式复杂，背景噪声强烈，难以及时有效地诊断。针对上述问题，提出基于稀疏分解的逐级匹配形态分析（Stagewise matching morphological component analysis，SMMCA）方法。稀疏分解是一种在超完备字典上对信号进行分解，并通过优化重构算法求解信号最稀疏表达的信号处理方法。逐级匹配形态分析基于稀疏分解理论，根据信号组成成分的差异，分别构造相应成分的稀疏字典，然后通过交替投影理论和逐级正交匹配追踪算法（Stagewise orthogonal matching pursuit。STOMP）对各组成成分进行优化重构，从而实现各成分的降噪和分离，准确快速地捕获信号中的特征信息。将提出的基于稀疏分解的逐级匹配形态分析方法应用于航空发动机轴承故障诊断，仿真和试验对比分析验证了该算法的有效性。

英文摘要：

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.