针对风电机组主轴承故障难以诊断的问题,提出一种基于带宽感知自适应啁啾模式分解(Bandwidth Aware Adaptive Chirp Mode Decomposition,BAACMD)和秃鹰算法优化直接快速迭代滤波(Bald Eagle Search Direct fast Iterative Filtering,BESDFIF)的故障诊断方法.首先采用加权频谱趋势法准确划分信号频段,诊断各频段的有效成分,随后利用模型拟合方法确定ACMD方法中惩罚因子α和初始中心频率f,并通过BAACMD方法实现对故障信号进行处理实现故障特征信息的提取;其次利用秃鹰优化算法对DFIF方法中影响参数及分量选取过程进行寻优;最后使用最优滤波区间参数的BESDFIF方法对所得分量进行分解降噪处理,从中诊断出微弱的风电机组主轴承故障特征频率成分.现场数据分析结果表明,所研究方法可以有效诊断风电机组主轴承的微弱故障特征,实现风电机组主轴承的故障诊断.
Wind turbines based on BAACMD and BESDFIF spindle bearing damage detection
Aiming to solve the problem that it is difficult to diagnose the main bearing fault of wind turbine,a fault diagnosis method based on BAACMD and BESDFIF is proposed.Firstly,the weighted spectrum trend method is used to accurately divide the signal frequency band and diagnose the effective components of each frequency band,the penaltyfactor and the initial center frequency f in the ACMD meth-od are determined by the model fitting method,and the fault feature information is extracted by processing the fault signal by the BAACMD method.Then the vulture optimization algorithm is used to optimize the filter interval parameters in the DFIF method.Finally,the BESDFIF method with optimal filtering interval parameters is used to decompose and reduce the noise of the obtained components,from which the weak fault characteristic frequency components of wind turbine main bearings are diagnosed.The field data anal-ysis results show that the research method can effectively diagnose the weak fault characteristics of the wind turbine main bearing and realize the fault diagnosis of the wind turbine main bearing.
wind turbine main bearingbald eagle searhdirect fast iterative filteringfault diagnosis