【摘要】 在噪声的影响下，齿轮的故障信息不易被识别。同步压缩小波包变换(synchrosqueezed wave packet transform, SSWPT)作为一种新的时频分析方法，具有良好的抗噪声能力。在其基础上提出基于SSWPT边际谱特征信息提取的齿轮故障诊断方法。首先，对故障齿轮的振动信号进行SSWPT得到信号的能量矩阵，并对能量矩阵进行积分变换求取齿轮振动信号的边际谱；然后，根据边际谱提取啮合频率及其倍频，并选择对应的啮合调制频带对能量矩阵运用同步压缩小波包逆变换(synchrosqueezed wave packet inverse transformation, ISSWPT)进行信号重构；最后，对重构信号进行解调分析，从而可以有效提取齿轮故障特征频率。仿真及试验分析结果表明，该方法可以准确地提取齿轮故障特征信息，且分析效果优于包络谱和基于快速谱峭度的共振解调方法，为齿轮的故障特征提取提供一种有效的方法。更多还原

【Abstract】 Under the influence of noise, gear fault information is difficult to be identified. As a new time-frequency analysis method, synchrosqueezed wave packet transform(SSWPT) has good ability to restrain noise effect. A gear fault diagnosis method based on SSWPT marginal spectrum feature extraction was proposed. Firstly, the vibration signal of gear fault was transformed into an energy matrix by SSWPT and the marginal spectrum of the gear vibration signal was obtained by the integration of the energy matrix. Then, the meshing frequency and its multipliers were extracted by the marginal spectrum of SSWPT and the energy matrixes were reconstructed through inverse synchrosqueezed wave packet transformation(ISSWPT). Finally, the reconstructed signal was demodulated and analysed, and the fault features of the gear were effectively extracted. The simulated and experimental results show that the proposed method is better than the envelope spectrum method and the resonance demodulation method based on fast kurtogram. It can accurately extract gear fault feature information and provides an effective way for gear fault diagnosis.更多还原