【摘要】 针对齿轮的故障振动信号的非平稳、非线性特征,采用非线性信号分析方法排列熵算法计算振动信号的排列熵大小来反映信号的复杂度。单通道的信息源难以反映出设备的真实运行状态,采用同源信息融合技术对双通道振动信号进行同源信息融合,计算融合后的信号的排列熵,进而提出了一种基于全矢排列熵(FVPE)的齿轮故障特征提取方法,通过实验模拟齿根裂纹、断齿和缺齿这三种故障状态,实验结果表明本方法有效地解决了单一通道信息源不完善造成的误诊难题,并可以很好地区分三种故障。更多还原

【Abstract】 Gear fault vibration signals are often non-stationary and non-linear,and the permutation entropy can well reflect the level of disorder for a one-dimensional time series and the dynamic behavior of mutant signals.However,the traditional fault diagnosis method based on a single source of vibration signals can’t ensure the integrity of the information.Here,the permutation entropy algorithm was used to analyze a two-channel homologous signal and to extract gear fault features based on full vector permutation entropy.Test results showed that this method can effectively reflect the mutation of signals and avoid misdiagnosis caused by a single channel imperfect information.更多还原