基于AE-BN的发电机滚动轴承故障诊断

Fault diagnosis of generator rolling bearing based on AE-BN

为解决风力发电机在复杂工况及耦合性、不确定性条件下故障识别的准确性问题,提出了一种基于自动编码器(AE)与贝叶斯网络(BN)的AE-BN故障诊断方法。采用AE对电流信号进行特征提取,得到能够高度表征信号的特征分量;基于故障与特征之间的因果关系,建立由故障位置、故障状态和故障特征搭建的三层BN;将AE的特征分量与BN的拓扑结构相结合建立风力发电机故障诊断模型,解决故障诊断中的不确定性问题,提高多故障诊断的准确性。实验结果表明:所提方法能够对故障特征信号进行分析及诊断,精确辨识不同故障类型,相比K近邻算法等具有明显优势。

To solve the accuracy of fault identification of wind turbines under complex working conditions,coupling,and uncertainty,an AE-BN fault diagnosis method based on a auto-encoder(AE)and Bayesian network(BN)is proposed.AE is used to extract the characteristics of the current signal,and the characteristic component that can highly characterize the signal is obtained;based on the causal relationship between fault and feature,a three-layer BN composed of fault location,fault state,and fault feature is established;The wind turbine fault diagnostic model is then established,the uncertainty problem in fault diagnosis is solved,and the precision of multi fault diagnosis is enhanced by combining the characteristic component of AE with the topology of BN.Experimental results show that the proposed method can analyze and diagnose fault characteristic signals and accurately identify different fault types,which has obvious advantages over other algorithms.