一种基于概率分布估计的水电机组故障预警方法

A HYDROELECTRIC-GENERATOR UNIT FAULST EARLY WARNING METHOD BASED ON DISTRIBUTION ESTIMATION

为了实现水电机组自动故障预警,提出一种基于概率分布估计的新方法。与以往试图学习机组故障样本方法不同的是,该方法学习机组正常状态而非故障状态,它把机组振动看作符合某一概率分布的独立同分布观测样本,利用Sch?lkopf 提出的单类支持向量机方法得到机组振动模式,并由此模式可以对测试观测进行预警。该方法直接对训练数据进行处理,不需进行复杂的预处理,并且简单,快速,并且对于水电机组的数据缺失和运行参数变化具有很好的适应能力。对甘肃大峡实际观测数据的仿真结果表明,概率分布估计可以有效的学习机组振动模式并对机组故障进行预警,为故障预警提出了一种新途径。

In order to realize hydroelectric-generator’s auto fault early warming, a new method based on distribution estimation is presented. Contrasting to other classical methods, the training data this method used are not fault data, but history data under normal operation. Generator’s vibration is looked as independently identical distribution observation samples which drawn from an underlying probability distribution and with this assumption generator’s vibration pattern can be carried out by using Sch?lkopf’s one class support vector machine method. Then with this pattern fault early warming can be carried out when new observation comes. This method has the property of dealing observation directly without complicated pretreatment, rapidity, and simplicity, it also has adaptive capacity to the data missing and change of operation parameters. Simulation on real observation shows its ability of learning generator’s normal vibration pattern and early warning fault pattern. Distribution estimation provides a new way for fault early warming.