基于包络S变换时频图像提取齿轮故障特征

齿轮故障振动信号中调幅和调频现象同时存在，其频谱包括以啮合频率及其谐波为载频，齿轮所在轴转频及其谐波为调制频率产生的调制边频带。针对齿轮故障振动信号特征提取困难的问题，提出一种基于包络分析和S变换时频图像相结合的故障特征提取方法。通过变速器齿轮故障模拟实验，采集齿轮正常、轻微磨损和严重磨损时的稳态振动信号，对其进行Hilbert变换得到信号的包络，然后对包络信号进行S变换，得到包络的时频图像的等高线灰度图像，计算图像的灰度共生矩阵及其统计特征量，提取齿轮故障特征。试验结果表明：该方法能有效提取齿轮故障特征。

Extraction of Gear Fault Feature Based on the Envelope and Time-frequency Image of S Transformation

When a gear has a local fault, the vibration signals of gear may contain amplitude and phase modulation. Their spectrums contain meshing frequencies, harmonics, and coupling frequencies generated by modulation. According to the characteristics of vibration signals from the gears with faults, a feature extraction method based on the envelope and time-frequency image of S transformation was proposed. Firstly, vibration signals from gears with differ worn degrees were collected by fault simulation in an experimental setup. Then, envelopes were obtained by Hilbert transform from vibration signals and envelopes of time-frequency contour maps were achieved by S transformation. Finally the features were extracted by calculating statistic parameters based on the grey-scale matrixes (GLCMs) of the maps. The result shows that the proposed method can effectively extract gear fault feature.