基于PSO-VMD-MCKD方法的风机轴承微弱故障诊断

PSO-VMD-MCKD Based Fault Diagnosis for Incipient Damage in Wind Turbine Rolling Bearing

针对风机滚动轴承微弱故障信号所具有的非线性和非平稳特征及易被强背景噪声掩盖的特点,提出了一种变分模态分解(variational modal decomposition,简称VMD)和最大相关峭度解卷积(maximum correlated kurtosis deconvolution,简称MCKD)相结合的滚动轴承微弱故障诊断方法。为实现VMD和MCKD的参数自适应选择,采用粒子群优化算法(particle swarm optimization,简称PSO),对两种算法中的参数进行优化。首先,利用PSO优化VMD算法中的α和K,再基于VMD对微弱故障信号分解后的结果,选取最优模态分量;其次,利用PSO优化MCKD算法中的L和T,再基于MCKD算法加强最优分量信号中的故障冲击成分;最后,通过包络谱提取出轴承微弱故障特征。仿真和试验均表明,此方法能够自适应增强轴承微弱故障中的冲击成分,有效提取出被强噪声淹没的轴承微弱故障特征。

The incipient damageof wind turbine rolling bearingsis very difficult to be detected,because the fault signalsare nonlinear,nonstationary,and likely to be buried by strong background noise.In light of this problem,a comprehensive methodology that combines variational modal decomposition(VMD)and maximum correlated kurtosis deconvolution(MCKD)is presented.The parameters of VMD and MCKD are selected automatically by the particle swarm optimization algorithm(PSO).First,the optimalαand K in VMD are calculated by PSO,and the most sensitive modal is selected according to the VMD decomposition of incipient fault signals.Then,theoptimal L and T in MCKD algorithm are calculated by PSO so as to boost the fault shock in the modal.Finally,the incipient fault feature is extracted from the envelope demodulation of the faults.Simulation results as well as experimental tests both validate that the proposed method can adaptively enhance the weak fault component of rolling bearing,thus can effectively extract incipient fault features of rolling bearings from strong background noise.