基于cICA-Kurtogram的变转速齿轮故障特征提取

Gear fault feature extraction based on cICA-Kurtogram under variable speed

在变转速齿轮故障特征提取过程中,针对约束独立分量分析对源噪声免疫能力差的问题,提出一种将约束独立分量分析、计算阶次跟踪和快速谱峭度相结合的方法。根据电机瞬时转频,获得故障齿轮的瞬时啮合频率,建立矩形波参考信号;利用约束独立分量分析提取故障特征更明显的时域信号,即相应的独立分量;然后基于故障齿轮所在轴的瞬时相位,通过等角度重采样将时域相应的独立分量转换为角域平稳信号;从阶次谱中提取故障齿轮特征阶次;根据快速峭度谱选择合适的滤波器参数对角域相应的独立分量进行滤波,获得平方包络谱,以判断故障齿轮所在轴的位置。结果表明,该方法是一种适用于变转速工况下齿轮箱振动状态监测与故障诊断的有效方法。

Constrained independent component analysis(cICA)is very sensitive to source noise in the fault feature extraction of gear fault under varying speed.For this problem,a method combining cICA,computed order tracking(COT)and fast-Kurtogram was proposed.According to the instantaneous rotating frequency of the motor,the instantaneous meshing frequency of the fault gear was obtained to establish rectangular wave as reference signal.The time domain signal with more obvious fault features was extracted by cICA,namely the independent component of interest(ICI).Then ICI in the time domain was converted into angular stationary signal by equal-angle resampling based on the instantaneous phase of the shaft,where the failed gear was located.Feature orders of fault gear were extracted in order spectrum.According to the fast-Kurtogram,the parameters of appropriate filter were selected and the ICI in the angular domain was filtered.The position of shaft,where the fault gear located,was determined by square envelope spectrum.Experimental results showed that this method was effective for monitoring and fault diagnosis of gearbox vibration under variable speed.