一种改进的MRVM方法及其在风电机组轴承诊断中的应用

针对风力机电组轴承故障难以诊断的问题,提出一种基于改进多分类相关向量机(MRVM)的风力机电组主轴轴承概率性智能故障诊断方法.首先,为了减少人为设定核参数的主观性以提高其分类性能,提出MRVM最优核参数自适应选取方法;然后,通过仿真实验结果验证所提方法的有效性及优越性;最后,以风电机组主轴滚动轴承故障诊断为实例,提取小...

AN IMPROVED MULTI-CLASS RELEVANCE VECTOR AND ITS APPLICATION TO WIND TURBINE BEARING DIAGNOSIS

In order to solve the fault diagnosis problem of wind turbine rolling bearings,a novel intelligent fault diagnosis method based on imprvoed multi-class relevance vector machine(multiclass relevance vector machine,MRVM)was proposed.Firstly,a new technique to optimize MRVM kernel function parameters was adopted to eliminate the subjectivity of parameter selections,and then the simulation experiment results verify the effectiveness and superiority of this technique.Finally,the spindle bearings fault diagnosis experiment was implemented. The wavelet packet energies of rolling bearing vibration signal were extracted as fault features. Experimental analysis show that the proposed method not only can improve the accuracy and efficiency of fault diagnosis,but also can output the probability information of fault diagnosis,which the probability information provide a reference for maintenance staff. In addition,the comparison experiments with other methods further indicate the superiority of the proposed method in intelligent fault diagnosis.