基于GA-ENN特征选择和参数优化的双馈风电机组轴承故障诊断

BEARING FAULT DIAGNOSIS USING GA-ENN BASED FEATURE SELECTION AND PARAMETERS OPTIMIZATION FOR DOUBLY-FED WIND TURBINE

针对双馈风电机组轴承时域、频域和时频域3种复合故障混合域特征集中的信息冗余或不相关性信息的干扰,导致故障诊断技术存在诊断时间长,诊断精度差的问题,提出一种基于遗传算法(GA)和Elman神经网络(ENN)相结合进行特征选择和参数优化实现故障诊断的新方法。为减小冗余度和不相关信息,采用GA进行特征选择,选出最优特征子集,根据识别误差最小和特征子集数目最少,构造ENN的适应度函数;为更精确识别轴承故障,采用GA优化ENN的权值和阈值参数,再进行故障识别,实例结果表明该方法对故障诊断的有效性和准确性。

Aiming at the problem of the long diagnosis time and the poor accuracy caused by the information redundancy or irrelevant information in the fault feature indicator sets from time domain,frequency domain and time-frequency domain of the doubly-fed wind turbine bearing,a new method for feature selection and parameter optimization of fault diagnosis is proposed,which combined Genetic Algorithm(GA)with Elman neural network(ENN). In order to reduce the redundancy and irrelevant information,is used for feature selection to select the optimal feature subset. According to the minimum recognition error and the minimum number of feature subsets,the fitness function of ENN is constructed. To identify the bearing fault,the GA optimizes the ENN weight and threshold parameters,and then identifies the fault. The example results shown the effectiveness and accuracy of the method for fault diagnosis.