基于多源信息融合-相关向量机的风力发电机故障诊断

为了能够全面准确地识别风力发电机的故障类别,考虑信号源振动和电流之间的相关性,提出了一种基于信息融合和改进相关向量机相结合的故障诊断方法。通过直驱风力发电机试验台实测数据,提取具有较高敏感度的特征参数作为诊断样本,建立基于振动和电流的改进相关向量机诊断模型进行初步故障诊断。利用信息融合建立多信号源故障诊断模型,获得最终风机故障诊断结果。试验表明,与基于单一信号的故障诊断方法相比,该方法具有更高的准确性,能很好地识别具有机电耦合特性的风力发电机故障类型。

Research on Fault Diagnosis of Windpower Generator Based onMulti-Source Information Fusion and Correlation Vector Machine

In order to accurately identify the fault type of wind turbines, this paper considers the correlation between vibration and current signal sources, presents a fault diagnosis method of information fusion and a new relevance vector machine. Got the data from wind power generator test bench, extracting data with higher sensitivity characteristic parameters of diagnosis as samples, then built an improved vibration and current detection based on relevance vector machine model of fault diagnosis. The model of multi signal source fault diagnosis is established by means of information fusion, and finally the fault diagnosis result of wind turbine is obtained. The experimental results show that the proposed method is more accurate and can identify the fault types of wind turbines with electromechanical coupling characteristics compared with the single signal based fault diagnosis method.