Infogram和参数优化CYCBD在滚动轴承复合故障特征分离中的应用

Application of the Infogram and parameter optimization CYCBD in rolling bearing composite fault feature separation

针对滚动轴承复合故障特征难以分离的问题,提出了一种基于Infogram和参数优化最大二阶循环平稳盲解卷积(maximum second-order cyclostationarity blind deconvolution,CYCBD)的复合故障特征分离方法。首先,采用Infogram方法分析故障信号,选取最优带通滤波器,获得冲击性和循环平稳性最强的频带信号;其次,根据理论故障频率,设定CYCBD的循环频率集,并以包络谱稀疏度为依据,自适应选择CYCBD的滤波器长度;再次,对获得的频带信号进行解卷积运算,提取不同频率的故障冲击成分,实现故障分离;最后,对分离出的各故障成分进行包络解调分析,根据故障特征频率,识别故障类型。通过对仿真信号、西安交大-昇阳科技联合实验室(Xi’an Jiaotong University-Changxing Sumyoung Technology,XJTU-SY)的轴承试验数据分析,证明了所提方法可以有效实现故障特征分离。在此基础上,通过自制试验平台实测数据,进一步论证了该方法的可行性。

To solve the difficulty in separating the composite fault features of rolling bearings,a composite fault feature separation method was proposed based on the methods of the Infogram and parameter optimization for maximum second-order cyclostationarity blind deconvolution(CYCBD).The Infogram method was adopted to analyze the fault signal and an optimal bandpass filter was selected to obtain the corresponding frequency band signal with the strongest impact and cyclic stationarity.The cyclic frequency set of CYCBD was set according to the theoretical fault frequency,and the filter length of CYCBD was adaptively selected according to the sparsity of envelope spectrum.A deconvolution operation was carried out on the acquired frequency band signals to extract fault shock components of different frequencies to realize fault separation.Finally,the separated fault components were analyzed by virture of the envelope demodulation,and the fault types were identified according to the fault characteristic frequency.Through the analysis on simulation signals and bearing experimental data from Xi’an Jiaotong University-Changxing Sumyoung Technology(XJTU-SY),the proposed method was proved to be able to effectively achieve the separation of fault characteristics.On this basis,the effectiveness of the method was further verified by the measured data on the self-made experimental platform.