基于优化变分模态分解及分形理论的风力机轴承故障诊断研究

Research on Fault Diagnosis of Wind Turbine Bearing based on Optimized Variational Mode Decomposition and Fractal Method

为实现强非线性特征风力机轴承振动信号的故障诊断,基于能量残差及粒子群优化算法提出优化变分模态分解方法(OVMD),通过峭度与相关系数对分解所获各模态进行筛选以剔除无效分量后重塑振动信号。引入分形理论,分别计算滤除无关模态前后轴承不同工作状态随负载变化时分形盒维数。结果表明:经OVMD分解后未滤除无关模态的信号在区分轴承不同工况时,各电机负载下盒维数出现混叠现象,干扰对轴承故障状态的判别与分类;而采用OVMD分解滤除无关模态后重组的信号,其分形盒维数在各种负载下均可实现对轴承工作状态的识别。

In order to realize fault diagnosis of vibration signals of wind turbine bearing with strong nonlinear characteristics,an optimized variational mode decomposition method is proposed based on energy residuals and particle swarm optimization algorithm.Each mode obtained by decomposition is screened by kurtosis and correlation coefficient to reshape the vibration signal after rejecting invalid components.The fractal theory is introduced to calculate the fractal box dimension when the different working conditions of the bearing change with load before and after filtering out unrelated modes.The results show that when the signals are not filtered and with the unrelated modes after OVMD decomposition are used to distinguish different working conditions of the bearing,under the load of each motor,the box dimension appears aliasing phenomenon,interfering with the discrimination and classification of bearing fault conditions.Once the unrelated modes are filtered out and the signals are recombined after OVMD decomposition,the fractal box dimension can be used to identify the working state of the bearing under various loads because the influence of irrelevant components are eliminated.