基于D-S证据融合的风力发电机组的故障预测

针对风力发电机组在机械和电气方面的故障，提出一种基于电气特征向量和振动特征向量的D-S证据融合方法。在2种信号的特征空间下分别构造2个经过参数优化的支持向量机，经过D-S融合之后给出最终的预测故障种类。相对于传统发电机故障诊断中分别针对机械故障和电气故障安装振动传感器和电流传感器，通过频谱特征来区分不同故障，证据融合方法能将电流信号用于机械故障的诊断，也能将振动信号用于电气故障的诊断。通过大量实测数据分析验证，本文提出的融合模型相比于只用单一信号构造的故障分类器具有更高的分类准确率。

Fault Prediction of Wind Turbine Based on D-S Evidence Fusion

Aiming at the mechanical and electrical faults of wind turbine generator, this paper presents a D-S fusion model based on electrical feature vector and vibrational feature vector. We construct two parameter-optimized support vector machines, as two evidences to predict the final fault pattern. Compared with the traditional fault diagnosis of generator for mechanical fault and electrical fault with vibration sensor and current sensor to distinguish different faults by spectrum characteristics, evidence fusion method can make current signal used for mechanical fault diagnosis, also vibration signal can be used for electric fault. Through a large number of experimental data analysis, fusion model compared with only a single signal structure has higher classification accuracy.