一种POA-VMD和自编码器结合的风电机组轴承劣化指标构建及故障诊断方法

A Method for Wind Turbine Bearing Deterioration Index Construction and Fault Diagnosis by Combining POA-VMD and Autoencoder

针对目前轴承性能劣化指标的构建及故障诊断高度依赖专家经验,限制条件繁多,实际应用情景单一的问题,提出一种鹈鹕优化算法(POA)优化的变分模态分解(VMD)和自编码器结合的风机轴承劣化指标构建及故障诊断方法。首先利用POA-VMD算法将轴承振动信号采用自适应方法分解为K个固有模态分量(IMF),并针对上述分量分别构建K个自编码器;然后以正常状态振动信号的分解结果为训练样本完成自编码器的训练,并以训练完成后模型的输出结果为基础构建轴承劣化指标,借助劣化指标监测轴承早期微弱故障;最后对故障时刻振动信号的IMF分量重构结果进行包络谱分析,确定故障的类型。经实验验证:该方法不仅可以清晰地展现轴承的劣化过程,对早期微弱故障敏感性高,而且在故障发生后可以准确诊断出故障类型。

In order to solve the problems that the construction and fault diagnosis of bearing performance deterioration index are highly dependent on expert experience,many constraints and single practical application scenarios,a method for the construction and fault diagnosis of wind turbine bearing deterioration index was proposed,which combined the pelican optimization algorithm(POA),variational mode decomposition(VMD),and autoencoder.Firstly,the POA-VMD algorithm was used to decompose the vibration signals of the entire lifespan of the bearing into K intrinsic mode functions(IMF)by the adaptive approach,and K individual autoencoders were constructed for each IMF to capture their distinctive characteristics.Then the autoencoders were trained with the decomposed results of the normal vibration signals as the training sample,and bearing degradation index was constructed based on the output result of the model after the training was completed,and the early weak failure of the bearing was monitored with the help of the deterioration index.Finally,the envelope spectrum analysis of the IMF component reconstruction results of the vibration signal at the time of fault was carried out to determine the fault type.Experimental results validate that this method can not only clearly show the deterioration process of the bearing,but also have a high sensitivity to early weak faults,and can accurately diagnose the type of fault after the fault occurs.