基于信息融合的风电机组齿轮箱轴承故障诊断

Fault diagnosis of wind turbines gearbox bearings based on information fusion

针对风电齿轮箱轴承故障问题,提出一种基于信息融合将BP神经网络与D-S证据理论相结合的风电轴承故障诊断方法。首先基于大数据,挖掘SCADA(supervisory control and data acquisition)系统中与风电齿轮箱轴承故障有关的振动、温度、电流、转矩和转速信号等故障特征;然后将各信号故障特征量作为神经网络输入,将神经网络的输出归一化作为证据理论基本概率分配值(BPA值),为解决各证据之间冲突问题,采用一种基于加权的方法来改进各条证据,以减小冲突;最后利用组合规则将各条改进的证据融合,得出最终诊断结果。研究基于某风场2MW风电机组的实际运行数据,结果表明:随着融合信号维度的增加,最终诊断结果的准确率也逐步提高,融合多维信号的可靠性明显高于单一信号。

A fault diagnosis method for wind turbines gearbox bearings was proposed based on information fusion combining BP neural network and D-S evidence theory.Firstly,based on big data,the fault characteristics of vibration,temperature,current,torque and rotating speed signals related to the faults of wind turbines gearbox bearings in SCADA system were explored.Then,the fault feature quantity of each signal was used as the input of the neural network,and the outputs of the neural network were normalized as the Basic Probability Assignment(BPA value)of D-S evidence theory.In order to solve the conflict between evidences,a weighted-based improvement method was used to improve the evidence.Finally,the combination rules were used to fuse the improved evidences to obtain final diagnosis results.The study was based on actual operating data of a 2MW wind turbine in a wind farm,and the results show that as the dimension of the fusion signal increases,the accuracy of the final diagnosis will gradually increase.The reliability of fusing multi-dimensional signals is significantly higher than that of single signals.