基于灰色粗糙集理论的风电机组传动链智能故障诊断方法

风电机组的状态监测和故障诊断是保证机组长期稳定运行和安全发电的关键。风电机组传动链系统的故障种类繁多，原因复杂，其故障征兆、故障原因和故障机理之间存在着极大的不确定性。文中在其故障诊断过程中，首先利用粗糙集原理对其特征参数进行约简，去除冗余参数，再利用粗糙集理论定量确定各特征参数的重要程度；根据约简的特征参数和各参数的重要程度，利用灰色关联度分析方法确定标准故障状态与目前机组状态的关联度，从而找到其故障之处。实例计算表明：在风电机组的故障诊断中将灰色系统理论和粗糙集理论结合是一种有效的方法，为其今后开展智能故障诊断提供了理论基础。

An Intelligent Diagnosis Method Based on Grey Rough Set Theory for Wind Turbine Driving Chain

The condition monitoring and fault diagnosis are crucial to ensure the long-term safe and stable operation of wind turbines. The transmission chain system of the wind turbine is prone to have various faults with complicated causes, and the fault symptom, failure causes and the failure mechanism are very uncertain in different working conditions. As indicated in this paper, in the process of the fault diagnosis, rough set theory is used to reduce superfluous attributes and quantitatively determine the relative importance of the attributes, and then Grey correlation analysis is used to calculate the Grey correlation degrees of all the standard fault states with respect to the current state according to reduced attributes and their relative importance so as to find out the fauh. An example is used to demonstrate the feasibility of the method. The method lays the foundation for the intelligent fault diagnosis.