中文摘要：

针对强背景噪声及干扰源信号影响下滚动轴承故障特征难以检测的问题，提出一种基于改进奇异谱分解的形态学解调方法用于轴承故障诊断。首先，为了克服奇异谱分析按经验性选取嵌入维数长度的缺陷，采用一种新的自适应信号处理方法——奇异谱分解（Singularspectrumdecomposition，SSD）进行振动信号分析，该方法通过构建一个轨迹矩阵与自适应选择嵌入维数长度，将非平稳信号从高频至低频依次划分为若干个单分量信号。针对奇异谱分解在分量序列重构过程中两端数据会偏离实际数据值进而引起端点效应现象的问题，提出运用特征波形匹配延拓法对奇异谱分解进行改进，提高其对振动信号的分解质量，获得一系列更接近实际曲线的单分量序列。为准确提取单分量中蕴含的有用故障特征信息，提出一种基于特征能量比自适应确定结构元素最佳尺度的自互补顶帽变换对单分量信号进行形态学解调。最后，分析解调结果的频谱特征并提取突出频率成分，实现轴承故障类型的准确判别。仿真和实测信号分析验证了方法的有效性。

英文摘要：

Aiming at the difficulty of fault feature extraction of rolling bearing under strong background noise and interference sources, a morphological demodulation method based on improved singular spectrum decomposition （SSD） is proposed to detect bearing fault. Firstly, in order to avoid the empirical selection of embedding dimension length in singular spectrum analysis, a novel self-adaptive signal processing method named SSD is used to analyze the vibration signal. In this method, track matrix first is built and embedding dimension length is adaptively selected, and then several single components can be obtained by decomposing the non-stationary signal. However, boundary effect will appear in component reconstruction process of SSD. Aiming at this problem, a waveform matching extension method is used to improve the SSD, and several single components respectively located in high frequency to tow frequency can be obtained, which are closer to the actual components curve. Besides, in order to extract the useful fault characteristic information of single components, a self-complementary top-hat transform is proposed to analyze the single components, which its optimal structure elements scale can be obtained by feature energy ratio. Finally, the fault types of bearing can be accurately identified by the spectrum analysis of morphological demodulation results. Simulation and practical signal analysis prove the validity of the proposed method.