基于变分模态分解的风电齿轮箱轴承故障诊断方法

Fault Diagnosis Method for Wind Turbine Gearbox Bearing based on Variational Mode Decomposition

双馈异步风力发电机采用变转速变桨距的控制策略以保持风力最大功率捕获,风电齿轮箱时刻处于变速变载的恶劣工况,其关键部件极易受到损伤。针对齿轮箱轴承故障特征易受到风机变工况干扰的问题,提出了一种变分模态分解与瑞利熵相结合的特征分析方法,实现对风电齿轮箱高速轴轴承健康状态系数的估计。本文以双馈异步风机齿轮箱高速轴轴承作为研究对象,研究了其在变转速变负载工况下的外圈故障特性。通过对比振动信号的频域特征参数与小波包分解能量特征结果,证明变工况运行条件下变分模态分解与瑞利熵相结合的故障诊断方法能够有效地辨识故障轴承。

Doubly-fed asynchronous wind generators adopt the control strategy with variable speed and variable pitch to track the maximum power point of wind energy.In this circumstance,wind turbine gearboxes suffer under extreme conditions,which lead tough damage to the corresponding key components.This paper proposed a feature extraction method based on the variational mode decomposition and Renyi entropy for fault diagnosis,which is aimed at estimating variable conditions of the wind turbine.In particular,the high-speed shaft bearing on wind turbine gearbox is selected as the main research object,whose outer ring fault characteristics is studied under variable speed and variable load conditions.The experiments are performed to verify the efficiency and effectiveness of the proposed method,by comparing the frequency domain feature parameters of vibration signals with wavelet packet decomposition energy feature.