基于FastICA的大型风力机主轴承故障诊断研究

Fault diagnosis research on large wind turbine main bearing based on FastICA

文章提出了基于FastICA大型风力机主轴承故障诊断方法。根据大型风力机的运行特点,采用负熵最大化判据和牛顿迭代原理实现FastICA算法并将其应用于大型风力机主轴承的故障诊断中。诊断结果表明,该方法可有效地将大型风力机主轴承和轴流风机的振动信号进行分离,从而清晰地看出主轴承的振动特征频率,实现对风力机主轴承的故障诊断。

In this paper, a model for fault diagnosis of large wind turbine main bearing based on FastICA is presented. According to the operating characteristics of large wind turbine, FastICA is achieved by negentropy maximization criterion and Newton iteration theory, which is applied in fault diagnosis of large wind turbine main bearing. Diagnosis results show that, this method can separate vibration signals collected from large wind turbine main bearing and axial flow blower effectively. Characteristics vibration frequency can be seen clearly from the separated signal, which can achieve fault diagnosis of large wind turbine main bearing.