基于随机集含糊证据的风力发电机故障诊断方法

A Wind Turbine Fault Diagnosis Method Based on Vague Evidence of Random Set

针对大型风力发电机故障信息的复杂性和不确定性,提出了基于随机集信任测度和似真测度的含糊化方法。该方法将故障样本模式和故障待检模式进行含糊化,利用随机集的信任测度和似真测度对待检模式和样本模式进行匹配,实现数据级融合;然后将匹配的结果作为证据理论的信任函数和似真函数,进行特征级融合;最后将融合结果作为决策级证据理论的证据进行最终融合,从而得出诊断结果。风力发电机的算例验证了该方法的合理性和有效性。

To deal with the complexity and uncertainty of wind turbine fault information,a vagueness method based on the belief measure and plausibility measure of random set is proposed.Firstly,it makes the sampled mode and detected mode vagueness,and matches the sample mode with detected mode through belief measure and plausibility measure to realize data fusion.Then the matched result is used as belief function and plausibility function of evidence theory to realize feature fusion.Finally,the fusion result is used as evidence to be fused in the decision-making step,thus it can give a precise diagnosis result.The wind turbine example verifies the rationality and effectiveness of the proposed method.