自适应多点最优最小熵反褶积在风电齿轮箱轴承故障诊断中的应用

Self-adaption Multipoint Optimal Minimum Entropy Deconvolution for Bearing Fault Diagnosis of a Wind Turbine Gearbox

齿轮箱在风力发电机组传动系统中起着重要的作用,因此齿轮箱故障诊断是风力发电机组健康管理中的一个关键问题;考虑到齿轮箱振动信号的频谱复杂性,多点最小最优熵反褶积方法是一种简单有效的齿轮箱故障诊断方法,因为它不仅可以去除掉大量的背景噪声和振动干扰,与此同时还能突出微弱的轴承故障脉冲信号;但是该方法的性能在一定程度上取决于前置参数滤波器长度的选择,不合适的滤波器参数值可能会导致过滤不足或过度过滤的后果;为了解决这一问题,提出了一种基于樽海鞘优化算法的自适应最优最小熵反褶积方法,该方法可以自适应选择最优滤波器长度,从而达到最优滤波效果;最后,利用包络解调方法对最优滤波信号进行包络分析得到包络谱,从而揭示故障特征频率;通过对某风力发电机实验台齿轮箱信号的仿真和实验分析,说明了该方法的原理和有效性。

A gearbox plays a crucial role in the transmission system of wind turbines,gearbox fault diagnosis is a key issue in wind turbine health management.Aimed at the spectral complexity of gearbox vibration signals,a multipoint optimal minimum entropy deconvolution(MOMED)method is a simple and effective approach for gearbox fault diagnosis.This method not only eliminates the background noise and vibration interference,but also highlights the weak bearing pulse fault signals.However,the performance of this method depends on the appropriate selection of the pre parameter filtering length to a certain extent and inapproriate filtering parameters may cause the results of insufficient or excessive filtering.In order to address this issue,an adaptive optimal minimum entropy deconvolution(AMOMED)method based on the salp swarm algorithm(SSA)is proposed,which adaptively selects the optimal filter length and achieves the superior filtering effect.Finally,the envelope demodulation method is used to perform envelope analysis on the optimal filtered signal to obtain the envelope spectrum,thereby revealing the fault characteristic frequency.The principle and effectiveness of the proposed method are demonstrated through the simulation and experimental analysis of the gearbox signal on a certain wind turbine experimental platform.